Method and apparatus for identifying characteristic value

ABSTRACT

A characteristic value identification method and an apparatus therefor which can develop each model to be integrated into an entire model, similar to a product in which individual parts are combined, are provided. A functional model of a part is prepared based on a potential quantity and a flow quantity representing a strength and a quantity of energy applied to the part, a steady internal characteristic value of the functional model in a steady state is identified, and a transient internal characteristic value of the functional model in a transient state is identified by using the identified steady internal characteristic value. Furthermore, the functional model having the characteristic value identified by such an identification apparatus is incorporated into a virtual testing system as a virtual prototype, an internal characteristic value of the virtual prototype is evaluated by providing a driving operation condition and an environment condition, actual machine test data obtained by the driving operation condition and the environment condition are compared with the internal characteristic value, and a re-identification is performed depending on the comparison result if necessary.

TECHNICAL FIELD

[0001] The present invention relates to a characteristic valueidentification method and an apparatus therefor, and in particular to acharacteristic value identification method and an apparatus therefor ofa part composing a product.

BACKGROUND ART

[0002] There have been known two types of prior art characteristic valueidentification methods and apparatuses therefor in the following:

[0003] One is disclosed in the Japanese Patent Application No.6-140081,No.8-18946, No.9-287161, and the like. This prior art method adoptsmeans for identifying a natural frequency, a natural mode, and a modaldamping ratio based on measured data of a frequency response function(FRF) obtained by a vibration test of a tested object (part).

[0004] The other is a method called a characteristic matrixidentification method. This method adopts means for identifying threekinds of characteristic matrices, i.e. a mass matrix, a damping matrix,and a stiffness matrix, which are coefficients of an equation of motionformulated based on “equilibrium of forces” rule in a dynamics system,from time history measured data of an exciting force and a responseobtained by the vibration test of the tested object (part).

[0005] Generally, a machine has a mechanical power source generating akinetic energy in some way, and is composed of a large number of partssuch as a mechanism which transmits the mechanical power and a mechanismwhich takes advantage of the mechanical power for the work. Also, withthe parts composing the machine, parts based on a physical unit systemsuch as electric, mechanical, and fluid systems are organically unitedor combined.

[0006] The fact is that models which reproduce such a large number ofparts with many different theoretical backgrounds are individuallymodeled for the identification according to the physical unit system inwhich the parts are included or the application thereof, with a mutualtheoretical relationship being neglected regardless of high generalityof the individual part and mechanism. For this reason, it has beendisadvantageous that the above-mentioned two types of prior art based onthe techniques of the mechanical system can be applied only to aspecific phenomenon of mechanical system vibration.

[0007] Also, in the prior art, the model (part) has been identified onlyby one dimension “strength (flow quantity)” for two dimensionalquantities “quantity (potential quantity)” and “strength” whichprescribe energy. Namely, since both rules which govern each of the twokinds of quantities composing energy are not applied, continuity ofvelocity and acceleration has not been represented on the identifiedmodel, so that it was disadvantageously difficult to unite the partswith each other.

[0008] Furthermore, since the prior art only expresses a structure of amodel, i.e. a positional relationship of the characteristics composingthe model but not the functions of the characteristics, structuralphenomena which the characteristics are compounded to reveal, e.g. anatural frequency, a natural mode shape, or a characteristic matrix ofan entire structure can only be made clear by the identification, sothat it has been impossible to directly clarify the characteristicvalues governing the function of an object.

[0009] It is accordingly an object of the present invention to provide acharacteristic value identification method and an apparatus thereforwhich can develop models across different physical unit systems to beintegrated into an entire model, similar to a product in whichindividual parts are combined.

DISCLOSURE OF INVENTION

[0010] A basic process for identifying an internal characteristic valueof a functional model relating to a product and a part (hereinafter bothsimply referred to as a part) in the present invention is shown inFIG. 1. This diagram illustrates an identification method takingadvantage of the functional model being a modeling method including asteady characteristic value and a transient characteristic value.

[0011] First of all, the functional model modeled in a process 1originally includes the steady characteristic value and the transientcharacteristic value. Accordingly, the functional model identificationof the present invention according to claim 1, these internalcharacteristic values are separately carried out by a steadyidentification process 2 and a transient identification process 3.Namely, for a basic process of the identification, the transientidentification process 3 is executed after the steady identificationprocess 2, since the steady characteristic value is free frominterference of transient state and the transient state interferes withthe steady characteristic value.

[0012] The steady identification process 2 further includes thefollowing processes according to claims 2-5. Firstly, in a process 21, asteady functional model is obtained which reproduces a function,performance, and characteristic of a steady state from the functionalmodel of the part. This steady functional model turns into a modelincluding only a steady characteristic except a transientcharacteristic. In a process 22, a steady test of a part which forms anidentification subject is performed to collect steady test data. In aprocess 23, the steady internal characteristic value is identified byusing the test data to be provided to the functional model by a process24.

[0013] The transient identification process 3 includes the followingprocesses according to claims 6-9. Firstly in a process 31, a transienttest model of a part which forms an identification subject is prepared,and the test is performed to collect transient test data. In a process32, a transient internal characteristic value of the functional model isidentified by using the test data. As shown in a process 4, the steadyinternal characteristic value identified in the process 24 is reflectedto the transient identification process 3.

[0014] Hereinafter, along these processes, the test model and theidentification method for identifying the functional model will bedescribed taking a motor as an example enabling a brief description.

[0015] Firstly, the functional model of the process 1 common to eachclaim will be described by taking a DC (direct current) motor as anexample.

[0016] <Modeling of Motor>

[0017] (1) Basic function of motor

[0018] A basic function of a motor is that electric energy is inputted,and the energy is converted into a rotation energy to be outputted. Asfor the energy conversion at this time, a voltage V_(M) and a currentI_(M) are applied to the electric system of the motor whereby an angularvelocity ω_(M) and a torque T_(M) are outputted from the rotationsystem. Also, a loss characteristic, an accumulation characteristic, andan additional load are included in the motor as an internal load, sothat these determine a behavior and a loss characteristic of the motor.

[0019] A torque coefficient χ_(T)[Nm/A] expressed by the ratio ofcurrent and torque, and a velocity coefficient χ_(ω)[V/(rad/sec)]expressed by the ratio of angular velocity and induced voltage are knownbetween the electric system and the rotation system of the DC motor. Anideal motor model which directly converts an input/output state valuewith these coefficients being made a basic function can be expressed bythe following equation: $\begin{matrix}\left. \begin{matrix}{\chi_{T} = {\frac{T_{M}}{I_{M}} = {\frac{1}{2\quad \pi}\quad \frac{P}{a}\quad Z\quad \varphi \times 10^{- 8}}}} \\{{\chi_{\omega} \cong \frac{V_{M}}{\omega_{M}}} = {\frac{1}{2\quad \pi}\quad \frac{P}{a}\quad Z\quad \varphi \times 10^{- 8}}}\end{matrix} \right\} & \text{Eq.(1)}\end{matrix}$

[0020] In equation (1), P is the number of poles in armature, “a” is thenumber of armature parallel circuits, Z is the number of all conductors,and φ is all of the magnetic flux per pole. They are characteristicsdetermined by a motor structure. Also, the magnetic flux φ is determinedby the structure in a separately excited type motor which uses apermanent magnet for a field in the same way as above, and is influencedby a state value of the electric system in a self-excited motor having afield winding. Eq.(1) is a basic one common to a DC rotator applied to aDC generator as well.

[0021] A torque coefficient χ_(T) and a velocity coefficient χ_(ω) ofEq.(1) performs a physical unit system transformation from current intotorque, or from angular velocity into voltage in the same equation.Therefore, these coefficients will be hereafter called motor constantsexpressed by M_(M). As for the relationship between the motor constantsand the internal load, the electric system and the rotation system areconsidered to be the motor internal load with the coefficients beingbordered. A basic form of the motor model combining the relationship ofthe basic function of the motor constant M_(M) and the internal load isshown in FIG. 2.

[0022] In FIG. 2, the input/output state value of the electric system isconnected to a power supply, and the input/output state value of themechanical system is united with an external machine load. Also, as forthe flow of the state value, the lower flow represents a flow system inwhich the current I_(M) of the power supply is converted by M_(M) into aflow of the output torque T_(M) which drives the external load, whilethe upper flow represents a potential system in which the angularvelocity ω_(M) received from the external load is converted into themotor induced voltage by M_(M) to turn into a flow of the voltage V_(M)returned to the power supply.

[0023] Thus, the mechanical system and the electric system are united bythe strength of energy called a potential quantity and the quantity ofenergy called a flow quantity, thereby enabling energy rules of twodimensions, equilibrium of forces and the continuity of velocity, to besatisfied.

[0024] (2) Functional model of motor

[0025] An electric circuit diagram of the motor model is shown in FIG.3.

[0026] In the electric system of FIG. 3, R_(M) is a winding resistance,L_(M) is an inductance, and R_(C) is an insulation resistance of thewinding. Also, the rotation system has a moment of inertia J_(M), aviscous resistance D_(M), and a friction torque T_(MF). The functionalmodel expressing these relationships is shown in FIGS. 4(1) and 4(2). Itis to be noted that such a functional model itself has already beendisclosed in the Japanese Patent Application Laid-open No. 9-91334.

[0027] In FIG. 4(1), the right side of the motor constant M_(M) formsthe rotation system internal load, and the left side thereof forms theelectric system internal load. This model has a voltage drop E_(MB) of abrush which supplies the current T_(M) to the armature, and a frictiontorque T_(MF) of the rotation system as the additional load. When theinfluence of both is neglected, T_(MF) and E_(MB) can be assumed to be0. It is to be noted that X_(M) and X_(L) in FIG. 4(1) are internalstate values. Also, the following velocity correction coefficient δ_(M)is added to the motor constant M_(M) by taking into account thevariation of the torque coefficient χ_(T) and the velocity coefficientχ_(ω). $\begin{matrix}\left. \begin{matrix}{\delta_{M} = \frac{\chi_{\omega}}{\chi_{T}}} \\{M_{M} = \chi_{T}}\end{matrix} \right\} & \text{Eq.(2)}\end{matrix}$

[0028] A government equation of such a motor functional model in FIG.4(1) is given by the following equation: $\begin{matrix}{\begin{bmatrix}0 \\0 \\\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}{- J_{M}} & 0 & {- D_{M}} & M_{M} & {- 1} & 0 & {- T_{MF}} \\0 & {- L_{M}} & {{- \delta_{M}}M_{M}} & {- \left( {R_{M} + R_{c}} \right)} & 0 & R_{C} & {- E_{MB}} \\0 & 0 & 1 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & R_{C} & 0 & {- R_{C}} & 0\end{bmatrix}*\begin{bmatrix}{\overset{.}{x}}_{M} \\{\overset{.}{x}}_{L} \\x_{M} \\x_{L} \\T_{M} \\I_{M} \\1\end{bmatrix}}} & \text{Eq.(3)}\end{matrix}$

[0029] In equation (3), the 1st-2nd rows form state equations, and the2nd-4th rows form output equations.

[0030] It is to be noted that Eq.(3) can be derived from the followingprocess shown in FIG. 4(1);

[0031] A white circle adds the inputted state values to make the outputstate value;

[0032] At a black circle, the outputted state value is equal to theinputted state value;

[0033] A characteristic expressed by a square makes the product of theinput state value and the characteristic the output state value;

[0034] A white circle with a mark×outputs the product of the input statevalues or input signals;

[0035] A while circle with a mark−makes the state value negative;

[0036] A triangle integrates a differential quantity of the inputtedstate value to make the output state value;

[0037] A large white circle on which a variable name is writtenexpresses the state value generated in the functional model as anadditional load.

[0038] (3) Uniting with motor power supply

[0039] A battery forming the power supply of the motor is modeled. FIG.5 shows a circuit diagram of the battery, and FIG. 6 shows itsfunctional model. In the battery of FIG. 6, a voltage V_(B) and acurrent I_(B) are connected to the voltage V_(M) and the current I_(M)of the motor shown in FIG. 4(1). Also, the characteristic of FIG. 6 hasE₀ as an induced electromotive force of the battery, and R_(E) as aninternal resistance of the battery.

[0040] The government equation of the battery in FIG. 5 is given asfollows: $\begin{matrix}{\left\lbrack I_{B} \right\rbrack = {\begin{bmatrix}{- \frac{1}{R_{B}}} & {\frac{1}{R_{B}}E_{0}}\end{bmatrix}\quad\begin{bmatrix}V_{B} \\1\end{bmatrix}}} & \text{Eq.(4)}\end{matrix}$

[0041] Uniting the motor model and the battery model is given by thefollowing equation by substituting equation (4) for Eq.(3).$\begin{matrix}{\left\lbrack \quad \begin{matrix}0 \\0 \\\omega_{M} \\V_{M} \\I_{M}\end{matrix} \right\rbrack = {\left\lbrack \quad \begin{matrix}{- J_{M}} & 0 & {- D_{M}} & M_{M} & {- 1} & {- T_{MF}} \\0 & {- L_{M}} & {{- \delta_{M}}M_{M}} & {- \left( {R_{M} + {R_{C}R_{B}Y_{0}}} \right)} & 0 & {{R_{C}I_{0}} - E_{MB}} \\0 & 0 & 1 & 0 & 0 & 0 \\0 & 0 & 0 & {{- R_{B}}R_{C}Y_{0}} & 0 & {R_{C}I_{0}} \\0 & 0 & 0 & {R_{C}Y_{0}} & 0 & I_{0}\end{matrix} \right\rbrack*\left\lbrack \quad \begin{matrix}{\overset{.}{x}}_{M} \\{\overset{.}{x}}_{L} \\x_{M} \\x_{L} \\T_{M} \\1\end{matrix}\quad \right\rbrack}} & \text{Eq.(5)}\end{matrix}$

[0042] The 1st-2nd rows of equation (5) form the state equations, andthe 3rd-5th rows form the output equations. An abstracted equivalentadmittance Y₀ and a current source I₀ in Eq.(5) are expressed by thefollowing equations: $\begin{matrix}{Y_{0} = \frac{1}{R_{B} + R_{C}}} & \text{Eq.(6)} \\{I_{0} = {Y_{0}E_{0}}} & \text{Eq.(7)}\end{matrix}$

[0043] Thus, a characteristic value identification method according tothe present invention uses a functional model of a part based on apotential quantity and a flow quantity representing energy applied tothe part, comprises a first process for preparing that functional model,a second process for converting the functional model into a steadyfunctional model in a steady state to identify a steady internalcharacteristic value, and a third process for identifying a transientinternal characteristic value in a transient state of the functionalmodel by using the steady internal characteristic value (claim 1).

[0044] Thus, in the characteristic value identification method of thepresent invention, the functional model of the part is prepared by theenergy rules called the potential quantity and the flow quantity, andthe transient identification is performed after the steadyidentification of its internal characteristic value, thereby enablingaccurate identification of the characteristic values for all the partsand modeling of an integrated product.

[0045] The above-mentioned second process may include a first step fordetermining an internal characteristic value of at least one steady testmodel from the steady functional model, a second step for collectingsteady test data by performing a test corresponding to the steady testmodel, and a third step for identifying a steady internal characteristicvalue of the internal characteristic value based on the steady test data(claim 2).

[0046] The above-mentioned first step may determine the internalcharacteristic value from a government equation in the steady state ofthe functional model (claim 3).

[0047] Also, the above-mentioned third step may convert the governmentequation into a recurrence equation to determine the steady internalcharacteristic value from a recurrence coefficient of the recurrenceequation (claim 4), and may divide the steady internal characteristicvalue into a known factor and an unknown factor to identify the steadyinternal characteristic value of the unknown factor (claim 5).

[0048] Furthermore, the above-mentioned third process may include afirst step for determining an internal characteristic value of at leastone transient test model in a transient state of the functional model, asecond step for collecting transient test data by performing a testcorresponding to the transient test model, a third step for applying thesteady internal characteristic value to the internal characteristicvalue of the transient test model to generate transient phenomenonreproduction data, and a fourth step for correcting the transientphenomenon reproduction data based on an error between the transientphenomenon reproduction data and the transient test data, therebyidentifying a transient internal characteristic value (claim 6).

[0049] When the error does not lie within an allowable range, theabove-mentioned fourth step may repeatedly correct a predeterminedtransient internal characteristic value within the transient phenomenonreproduction data until the error lies within the allowable range, anddetermine the transient internal characteristic value to be identifiedwhen the error lies within the allowable range (claim 7).

[0050] Also, the above-mentioned fourth step may preliminarily calculatea variance deviation, as a time history sensitivity, to an initial valueat a time when each transient internal characteristic value is increasedor decreased at a fixed ratio, and select a transient internalcharacteristic value having a maximum sensitivity within the timehistory sensitivity as the predetermined transient internalcharacteristic value (claim 8), or may select a transient internalcharacteristic value having the time history sensitivity similar to theerror as the predetermined transient internal characteristic value(claim 9).

[0051] It is to be noted that the fourth step may simultaneously selecta plurality of transient internal characteristic values having differentmaximum sensitivity times as the predetermined transient internalcharacteristic value (claim 10).

[0052] Also, a characteristic value identification apparatus accordingto the present invention comprises block replacement means for afunctional model of a part, test reproduction means for reproducing atleast one steady test model in a steady state of the functional modeland at least one transient test model in a transient state, testingmeans of the part for performing a steady test and a transient testrespectively corresponding to the steady test model and the transienttest model, measurement means for collecting steady test data andtransient test data at a time when a steady test and a transient test ofthe part are performed by the testing means, and calculating means foridentifying a steady internal characteristic value of the steady testmodel by using the steady test data, for applying the steady internalcharacteristic value to the transient test model to generate transientphenomenon reproduction data, and for correcting the transientphenomenon reproduction data based on an error between the transientphenomenon reproduction data and the transient test data, therebyidentifying a transient internal characteristic value (claim 11).

[0053] Thus, it becomes possible to quickly identify the functionalmodel of the same kind.

[0054] When the error does not lie within an allowable range, theabove-mentioned calculating means may repeatedly correct a predeterminedtransient internal characteristic value within the transient phenomenonreproduction data until the error lies within the allowable range, anddetermine the transient internal characteristic value to be identifiedwhen the error lies within the allowable range (claim 12).

[0055] Also, the above-mentioned calculating means may preliminarilycalculate a variance deviation, as a time history sensitivity, to aninitial value at a time when each transient internal characteristicvalue is increased or decreased at a fixed ratio, and select a transientinternal characteristic value having a maximum sensitivity within thetime history sensitivity as the predetermined transient internalcharacteristic value (claim 13), or may select a transient internalcharacteristic value having the time history sensitivity similar to theerror as the predetermined transient internal characteristic value(claim 14).

[0056] It is to be noted that the calculating means may simultaneouslyselect a plurality of transient internal characteristic values having adifferent maximum sensitivity time as the predetermined transientinternal characteristic value (claim 15).

[0057] Furthermore, a virtual testing system according to the presentinvention incorporates the functional model, as a virtual prototype,having an internal characteristic value identified by a characteristicvalue identification apparatus claimed in claim 11 and comprisescondition assigning means for assigning a driving operation conditionand an environment condition to the characteristic value identificationapparatus, observation means for observing reproduction data obtained bythe virtual prototype when the driving operation condition and theenvironment condition are assigned, and evaluation means for evaluatingan observation result of the observation means (claim 16).

[0058] Namely, in the identification of the internal characteristicvalue by the present invention, an evaluation of a function, aperformance, and a characteristic of a part as well as a product in anactual machine test can be regarded as a virtual test performed on acomputer, and the actual machine test performed in a development processof designing, prototyping, and testing in the prior art can be omitted,so that it becomes possible to shorten the period and to reduce thedevelopment cost by the virtual test.

[0059] The above-mentioned virtual testing system may further compriseanother measurement means for measuring actual machine test data at atime when the driving operation condition and the environment conditionare provided to an actual machine which forms a subject of the virtualprototype, and re-identification means of the virtual prototype, whereinthe evaluation means may compare an output of the measurement unit andthe observation result, and make the re-identification means re-identifythe virtual prototype according to the comparison result (claim 17).

[0060] A fixed virtual prototype may be incorporated into a part of adrive system and a load system connected to the part as theabove-mentioned virtual prototype, the testing means may perform a testcorresponding to the fixed virtual prototype, and the evaluation meansat this time may make the re-identification means perform are-identification according to the comparison result (claim 18).

BRIEF DESCRIPTION OF DRAWINGS

[0061]FIG. 1 is a block diagram showing a basic concept of acharacteristic value identification method and an apparatus thereforaccording to the present invention;

[0062]FIG. 2 is a block diagrams showing a basic system of a motor modelwith a potential quantity and a flow quantity of energy being mutuallyrelated;

[0063]FIG. 3 is a diagram showing a general electric circuit of a motor;

[0064]FIG. 4 is a diagram respectively showing a functional model andsymbols of a motor used in a characteristic value identification methodand an apparatus therefor according to the present invention;

[0065]FIG. 5 is a diagram showing a general electric circuit of abattery;

[0066]FIG. 6 is a diagram showing a functional model of a battery;

[0067]FIG. 7 is a block diagram showing a process for preparing a steadytest model group from a motor steady functional model relating to acharacteristic value identification method and an apparatus thereforaccording to the present invention;

[0068]FIG. 8 is a diagram showing a steady functional model of a motor;

[0069]FIG. 9 is a diagram showing a motor model at the time of a steadyload test;

[0070]FIG. 10 is a diagram showing a motor model at the time of a loadtest;

[0071]FIG. 11 is a diagram showing a motor model at the time of a locktest;

[0072]FIG. 12 is a diagram showing a motor model at the time of ainduced voltage test;

[0073]FIG. 13 is a diagram showing a motor model at the time of abraking test;

[0074]FIG. 14 is a block diagram showing an identification process of asteady functional model from a steady test model group in acharacteristic value identification method and an apparatus thereforaccording to the present invention;

[0075]FIG. 15 is a graph showing a motor characteristic of a steadystate;

[0076]FIG. 16 is a diagram showing an external shape of a motor;

[0077]FIG. 17 is a graph showing a steady internal characteristic valueof a motor with an ordinate and an abscissa in FIG. 15 being exchanged;

[0078]FIG. 18 is a graph showing a steady internal characteristic valueof a motor;

[0079]FIG. 19 is a block diagram showing a transient identificationprocess of a functional model in a characteristic value identificationmethod and an apparatus therefor according to the present invention;

[0080]FIG. 20 is a diagram showing a structure of an electrically drivenarm mechanism;

[0081]FIG. 21 is a diagram showing a model at the time of an actual loadtransient test of a motor;

[0082]FIG. 22 is a diagram showing a transient test model at the timewhen a motor axis is fixed;

[0083]FIG. 23 is a diagram showing a fixed torque transient test modelof a motor;

[0084]FIG. 24 is a diagram showing a model at the time of an inertialrotation test of a motor;

[0085]FIG. 25 is a diagram showing a motor operation switch circuit;

[0086]FIG. 26 is a diagram showing a functional model of a motoroperation switch;

[0087]FIG. 27 is a diagram showing an electric circuit of a switchingmethod;

[0088]FIG. 28 is a diagram showing a functional model of an electriccircuit of a switching method;

[0089]FIG. 29 is a diagram showing an electric circuit of a fixedcurrent control;

[0090]FIG. 30 is a diagram showing a functional model of a fixed currentcontrol circuit;

[0091]FIG. 31 is a graph showing a trial simulation result beforeperforming a transient identification in a characteristic valueidentification method and an apparatus therefor according to the presentinvention;

[0092]FIG. 32 is a flow chart illustrating an operation of a transientidentification in a characteristic value identification method and anapparatus therefor according to the present invention;

[0093]FIG. 33 is a graph respectively showing a sensitivity of acharacteristic value in a characteristic value identification method andan apparatus therefor according to the present invention;

[0094]FIG. 34 is a graph respectively showing a relationship between acurrent deviation and a characteristic value time history sensitivity ina characteristic value identification method and an apparatus thereforaccording to the present invention;

[0095]FIG. 35 is a graph respectively showing a simulation result afterperforming an identification in a characteristic value identificationmethod and an apparatus therefor according to the present invention;

[0096]FIG. 36 is a graph respectively showing a simulation result of astart and a stop in a characteristic value identification method and anapparatus therefor according to the present invention;

[0097]FIG. 37 is a graph respectively showing a simulation result of aregular/reversed rotation and brake in a characteristic valueidentification method and an apparatus therefor according to the presentinvention;

[0098]FIG. 38 is a block diagram illustrating a characteristic valueidentification apparatus according to the present invention by taking amotor as an example;

[0099]FIG. 39 is a block diagram showing a relationship between aproduct development and a model;

[0100]FIG. 40 is a block diagram showing a concept of a virtual testaccording to the present invention;

[0101]FIG. 41 is a diagram showing a model at the time when a virtualtesting system according to the present invention is applied to a car;

[0102]FIG. 42 is a block diagram showing an embodiment of a virtualtesting system according to the present invention;

[0103]FIG. 43 is a block diagram showing an example applying a virtualtesting system according to the present invention to an actual machinetest of a transmission;

[0104]FIG. 44 is a model diagram showing a virtual testing system of adrive system, a transmission, and a load system; and

[0105]FIG. 45 is a block diagram showing an example applying a virtualtesting system according to the present invention to an actual machinetest of a power train.

DESCRIPTION OF REFERENCE NUMERALS

[0106] 1 motor model (block replacement structure)

[0107] 2 test reproduction model

[0108] 3 tested motor

[0109] 4 drive circuit unit

[0110] 5 load generator

[0111] 6 electric load

[0112] 8, 34 measurement unit

[0113] 9 calculation unit

[0114] 10 virtual test model

[0115] 11 virtual prototype

[0116] 12 test standard model

[0117] 13 engine model (motor model)

[0118] 14 body model (generator model)

[0119] 21 drive model (battery operation model)

[0120] 22 load model (load generator model)

[0121] 23, 37, 40 observation model, observation system

[0122] 24 characteristic value update model

[0123] 25 model replacement unit

[0124] 30 virtual test portion

[0125] 31, 38, 39 drive operation model, operation system

[0126] 32, 41 environment condition model, environment system

[0127] 33 actual machine test portion

[0128] 35 evaluation model

[0129] 36 control system input/output unit

[0130] 50 tested transmission

[0131] 51 tested power train (P/T)

[0132] 71 drive system control unit

[0133] 72 load system control unit

[0134] Throughout the figures, like reference numerals indicate like orcorresponding components.

BEST MODE FOR CARRYING OUT THE INVENTION

[0135] Hereinafter, an embodiment of a characteristic value (model)identification method and an apparatus according to the presentinvention schematically shown in FIG. 1 will be described.

[0136] 1. Functional Model in Steady State

[0137] In order to perform the steady identification process 2 shown inFIG. 1, the steady functional model conversion is required as shown inthe process 21. In this steady functional model conversion, it isnecessary as will be described hereafter to convert the functional modelinto the steady functional model for reproducing the steady state of thepart, and to further convert the steady functional model into the steadytest model according to steady tests of the part (process 210).

[0138] Since the steady identification utilizes the steady test data,the steady test model can make the steady functional model of theidentification subject accord with the tested object to which the actualmachine test is performed, by modeling the tested object according tothe condition and the contents of the steady test for generating thetest data. FIG. 7 shows a flow of a series of model conversions.

[0139] 1.1 Conversion from Function Model into Steady Functional Model

[0140] Since there is no transient variation in a steady state of amotor, neither the inductance L_(M) nor the moment of inertia J_(M)shown in FIG. 4A influences the steady internal characteristic value.Also, since the insulation resistance R_(C) in FIG. 4A has an extremelylarge value, the influence to the motor current I_(M) can be neglected.From the above-mentioned points, the motor model in the steady statewhere L_(M), J_(M), R_(C) of FIG. 4(1) are eliminated turns into afunctional model shown in FIG. 8.

[0141] The left side of FIG. 8 shows a battery model of FIG. 6 in whichthe operation switch of the electric system is omitted, and the rightside thereof shows a motor model in a steady state. From FIG. 8, themotor model in the steady state can be expressed by the followinggovernment equation: $\begin{matrix}{\begin{bmatrix}\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}{- \frac{1}{D_{M}}} & \frac{M_{M}}{D_{M}} & {{- \frac{1}{D_{M}}}T_{MF}} \\{- \frac{M_{M}\delta_{M}}{D_{M}}} & {R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} & {E_{MB} - {\frac{M_{M}\delta_{M}}{D_{M}}T_{MF}}}\end{bmatrix}\quad\begin{bmatrix}T_{M} \\I_{M} \\1\end{bmatrix}}} & \text{Eq.(8)}\end{matrix}$

[0142] For another method of deriving the steady government equation,the government equation in the steady state obtained by eliminatingL_(M), J_(M), R_(C) from the government Eq.(3) of the functional modelin FIG. 4(1) can be obtained by the following processes:

[0143] {circle over (1)} The equation obtained by eliminating thedifferential state values of {dot over (χ)}_(M) and {dot over (χ)}_(L)from Eq.(3) with L_(M) and J_(M) being assumed to be 0 is given asfollows: $\begin{matrix}{\begin{bmatrix}0 \\0 \\\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}{- D_{M}} & M_{M} & {- 1} & 0 & {- T_{MF}} \\{{- \delta_{M}}M_{M}} & {- \left( {R_{M} + R_{c}} \right)} & 0 & R_{C} & {- E_{MB}} \\1 & 0 & 0 & 0 & 0 \\0 & R_{C} & 0 & {- R_{C}} & 0\end{bmatrix}*\begin{bmatrix}x_{M} \\x_{L} \\T_{M} \\I_{M} \\1\end{bmatrix}}} & \text{Eq.(9)}\end{matrix}$

[0144] {circle over (2)} The equation obtained by eliminating theinternal state values of x_(M) and x_(L) from equation (9) is given asfollows: $\begin{matrix}{\begin{bmatrix}\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}{- \frac{R_{M} + R_{c}}{D}} & \frac{R_{C}M_{M}}{D} & \frac{{M_{M}E_{MB}} - {\left( {R_{M} + R_{c}} \right)T_{MF}}}{D} \\\frac{R_{C}\delta_{M}M_{M}}{D} & {- \frac{R_{C}\left( {{D_{M}R_{M}} + {R_{C}\delta_{M}M_{M}^{2}}} \right)}{D}} & \frac{R_{C}\left( {{\delta_{M}M_{M}T_{MF}} - {D_{M}E_{MB}}} \right)}{D}\end{bmatrix}\quad*\quad \begin{bmatrix}T_{M} \\I_{M} \\1\end{bmatrix}}} & \text{Eq.(10)}\end{matrix}$

[0145] “D” in equation (10) is assumed to be the following equation:

D=D _(M)(R _(M) +R _(C))+δ_(M) M _(M) ²  Eq.(11)

[0146] {circle over (3)} The equation obtained by eliminating theinsulation resistance by making R_(C)=∞ from Eq.(10) is given asfollows: $\begin{matrix}{\begin{bmatrix}\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}{- \frac{1}{D_{M}}} & \frac{M_{M}}{D_{M}} & {{- \frac{1}{D_{M}}}T_{MF}} \\\frac{\delta_{M}M_{M}}{D_{M}} & {{- R_{M}} - \frac{\delta_{M}M_{M}^{2}}{D_{M}}} & {{\frac{\delta_{M}M_{M}}{D_{M}}T_{MF}} - E_{MB}}\end{bmatrix}*\quad \begin{bmatrix}T_{M} \\I_{M} \\1\end{bmatrix}}} & \text{Eq.(12)}\end{matrix}$

[0147] The government equation (12) in the steady state derived from thegovernment Eq.(3) of the functional model is equal to the governmentequation (8) derived from the functional model in the steady state inFIG. 8.

[0148] Accordingly, there are two methods for obtaining the steadygovernment equation reproducing the steady state of the part; one byconverting the functional model reproducing the transient state of thepart into the steady functional model, and the other by deriving thesteady government equation from the government equation of thefunctional model. This indicates that the functional model in the steadystate can be reversely depicted from the derived steady governmentequation, and that there is a reversibility between the steadyfunctional model and the steady government equation.

[0149] 1.2 Conversion from Steady Functional Model into Test Model

[0150] A steady state of an actuality by the steady functional modelwill be considered, and the test model will be examined. The test modelin the steady state models the test of the actuality which provides aspecified condition to an input/output system of a tested object, fromthe steady functional model and its government equation. The steadyinternal characteristic value is identified by relating the test dataobtained when the specified condition is assigned to the tested object,with the government equation.

[0151] The relationship between the specified condition in which thesteady internal characteristic value is assigned to the motor model andthe test is as follows:

[0152] {circle over (1)} In case a fixed torque is provided to the motorwith the power supply being connected, the model reproduces therelationship between the load applied to the motor and the steadyinternal characteristic value, so that the relationship between themotor current, the angular acceleration, and the load torque can beobserved (steady load test: FIG. 9).

[0153] {circle over (2)} The case where the input torque T_(M) isassumed to be 0 corresponds to a motor rotation in an idle state, sothat an idle angular velocity and an idle current are observed (idletest: FIG. 10).

[0154] {circle over (3)} The case where an output angular velocity ω_(M)is assumed to be 0 corresponds to a lock state or a state at a startingmoment when the motor rotation is compulsorily stopped, so that a lockcurrent and a lock torque can be observed (motor lock test: FIG. 11).

[0155] {circle over (4)} In case the input current I_(M) is assumed tobe 0 and the input torque T_(M) is provided, the model which reproducesa generator function of the motor is obtained, so that the inducedvoltage and the angular acceleration can be observed (induced voltagetest: FIG. 12).

[0156] {circle over (5)} In case a resistor is connected to theinput/output state value of the electric system to provide a fixedtorque, the model which reproduces a regenerative braking force of themotor can be obtained, so that the relationship between the motorcurrent, the angular acceleration, and a braking torque can be observed(steady braking test: FIG. 13).

[0157] These test models are different from each other depending on howto provide the specified conditions. However, in an actual modeling, thevalues of the steady characteristic such as a steady functional model ora steady load test model and the input state are made 0 or an infinitevalue (large value within a range where the value does not influence areproduction result), so that each test model can be equivalentlyrealized. It is necessary to perform at least one of such steady tests.

[0158] In view of the above-mentioned description, the motor test modelperformed from the steady functional model in FIG. 8 under the specifiedconditions, is given as follows:

[0159] 1.2.1 Motor Steady Load Test

[0160]FIG. 9 shows a steady load test which observes the performance andthe characteristic by providing the load torque to the motor.

[0161] In FIG. 9, the input torque T_(M) is provided as a drive torqueby the additional load from the steady functional model in FIG. 8, andthe output angular velocity ω_(M) is represented as an observationquantity. Accordingly, the government Eq.(8) of the motor is transformedinto the following equation: $\begin{matrix}{\begin{bmatrix}\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}\frac{M_{M}}{D_{M}} & {{- \frac{1}{D_{M}}}\left( {T_{M} + T_{M\quad F}} \right)} \\{R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} & {E_{M\quad B} - {\frac{M_{M}\delta_{M}}{D_{M}}\left( {T_{M} + T_{M\quad F}} \right)}}\end{bmatrix}\quad\begin{bmatrix}I_{M} \\1\end{bmatrix}}} & {{Eq}.\quad (13)}\end{matrix}$

[0162] The government equation of the steady load test obtained bymutually substituting the government Eq.(4) of the battery for equation(13) is given as follows: $\begin{matrix}{\left\lbrack \quad \begin{matrix}\omega_{M} \\V_{M} \\I_{M}\end{matrix} \right\rbrack = \left\lbrack \quad \begin{matrix}{\frac{1}{D_{M}}\frac{{M_{M}\left( {E_{0} - E_{M\quad B}} \right)} - {\left( {R_{B} + R_{M}} \right)\left( {T_{M} + T_{M\quad F}} \right)}}{\left( {R_{B} + R_{M}} \right) + \frac{M_{M}^{2}\delta_{M}}{D_{M}}}} \\\frac{{\left( {R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} \right)\frac{1}{R_{B}}E_{0}} + E_{M\quad B} - {\frac{M_{M}\delta_{M}}{D_{M}}\left( {T_{M} + T_{M\quad F}} \right)}}{1 + {\left( {R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} \right)\frac{1}{R_{B}}}} \\\frac{E_{0} - E_{M\quad B} + {\frac{M_{M}\delta_{M}}{D_{M}}\left( {T_{M} + T_{M\quad F}} \right)}}{\left( {R_{B} + R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} \right)}\end{matrix}\quad \right\rbrack} & {{Eq}.\quad (14)}\end{matrix}$

[0163] Equation (14) is a government equation reproducing a rated loadtest of the motor, where all of the equations assume the output statevalue reproducing the state value of the steady state.

[0164] 1.2.2 Motor Idle Test

[0165] In the motor idle state, the input torque T_(M) of the steadyload test model in FIG. 9 becomes 0. The idle test model where the inputtorque T_(M) is eliminated from FIG. 9 can be shown by FIG. 10.

[0166] The government equation of the motor in FIG. 10 turns into thefollowing equation obtained by eliminating the load torque T_(M) fromthe government Eq.(13) of the steady load test: $\begin{matrix}{\begin{bmatrix}\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}\frac{M_{M}}{D_{M}} & {{- \frac{1}{D_{M}}}T_{M\quad F}} \\{R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} & {E_{M\quad B} - {\frac{M_{M}\delta_{M}}{D_{M}}T_{M\quad F}}}\end{bmatrix}\quad\begin{bmatrix}I_{M} \\1\end{bmatrix}}} & {{Eq}.\quad (15)}\end{matrix}$

[0167] The government equation of the idle test obtained by mutuallysubstituting the government Eq.(4) of the battery for equation (15) isgiven as follows: $\begin{matrix}{\left\lbrack \quad \begin{matrix}\omega_{M} \\V_{M} \\I_{M}\end{matrix} \right\rbrack = \left\lbrack \quad \begin{matrix}{\frac{1}{D_{M}}\frac{{M_{M}\left( {E_{0} - E_{M\quad B}} \right)} - {\left( {R_{B} + R_{M}} \right)T_{M\quad F}}}{\left( {R_{B} + R_{M}} \right) + \frac{M_{M}^{2}\delta_{M}}{D_{M}}}} \\\frac{{\left( {R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} \right)\frac{1}{R_{B}}E_{0}} + E_{M\quad B} - {\frac{M_{M}\delta_{M}}{D_{M}}T_{M\quad F}}}{1 + {\left( {R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} \right)\frac{1}{R_{B}}}} \\\frac{E_{0} - E_{M\quad B} + {\frac{M_{M}\delta_{M}}{D_{M}}T_{M\quad F}}}{\left( {R_{B} + R_{M} + \frac{M_{M}^{2}\delta_{M}}{D_{M}}} \right)}\end{matrix}\quad \right\rbrack} & {{Eq}.\quad (16)}\end{matrix}$

[0168] 1.2.3 Motor Lock Test

[0169] At the starting moment of the motor, the angular velocity ω_(M)becomes 0, where it can be regarded as a motor lock state in which themotor rotation is compulsorily stopped. Accordingly, the functionalmodel of the lock test reproducing the motor lock can be shown by FIG.11 where the output angular velocity ω_(M) of the steady functionalmodel in FIG. 8 is supposed to be 0. It is to be noted that since abrush voltage drop E_(MB) generated in the stop state of the motorrotation does not generate a fixed voltage drop but only assumes aminute voltage drop by a brush resistance, it is neglected and omitted.

[0170] In FIG. 11, each steady internal characteristic value relating tothe output angular velocity ω_(M) is eliminated from the steadyfunctional model in FIG. 8, so that the input torque T_(M) is shown bythe observation quantity of the lock torque. The government equation ofFIG. 11 is given as follows: $\begin{matrix}{\begin{bmatrix}V_{M} \\T_{M}\end{bmatrix} = {\begin{bmatrix}R_{M} \\M_{M}\end{bmatrix}\quad\left\lbrack I_{M} \right\rbrack}} & {{Eq}.\quad (17)}\end{matrix}$

[0171] The government equation of the lock test obtained by substitutingthe government Eq.(4) of the battery for equation (17) to be integratedis given as follows: $\begin{matrix}{\begin{bmatrix}V_{M} \\T_{M} \\I_{M}\end{bmatrix} = {\begin{bmatrix}{\frac{R_{M}}{R_{B} + R_{M}}E_{0}} \\{{- \frac{E_{0}}{R_{B} + R_{M}}}M_{M}} \\\frac{E_{0}}{R_{B} + R_{M}}\end{bmatrix}\quad\lbrack 1\rbrack}} & {{Eq}.\quad (18)}\end{matrix}$

[0172] 1.2.4 Motor Induced Voltage Test

[0173]FIG. 12 shows a model of an induced voltage test which providesthe torque to the motor to observe a counter electromotive force V_(M)induced by the armature and the angular velocity ω_(M).

[0174] In FIG. 12, the steady internal characteristic value relating tothe input current I_(M) is eliminated from the steady functional modelin FIG. 8, so that the output angular velocity X_(M) is shown by theobservation quantity. The government equation of FIG. 12 is given asfollows: $\begin{matrix}{\begin{bmatrix}\omega_{M} \\V_{M}\end{bmatrix} = {\begin{bmatrix}{{- \frac{1}{D_{M}}}\left( {T_{M} + T_{M\quad F}} \right)} \\{{- \frac{M_{M}\delta_{M}}{D_{M}}}\left( {T_{M} + T_{M\quad F}} \right)}\end{bmatrix}\quad\lbrack 1\rbrack}} & {{Eq}.\quad (19)}\end{matrix}$

[0175] 1.2.5 Motor Steady Braking Test

[0176]FIG. 13 shows a steady model of a regenerative braking whichprovides the torque to the motor and reversely returns the counterelectromotive force V_(M) induced by the armature to the motor through abraking resistance R_(L) to provide a braking force.

[0177] In FIG. 13, the battery is eliminated from the steady functionalmodel in FIG. 8, the braking resistance R_(L) is connected, and theinput torque T_(M) of the motor is provided as a drive torque. Inaddition, the output angular velocity ω_(M) is shown as the observationquantity. The government equation of the motor in FIG. 13 is given asfollows: $\begin{matrix}{\begin{bmatrix}\omega_{M} \\V_{M} \\I_{M}\end{bmatrix} = {\begin{bmatrix}{{- \frac{{M_{M}E_{M\quad B}} + {\left( {R_{L} + R_{M}} \right)\left( {T_{M} + T_{M\quad F}} \right)}}{\left( {R_{L} + R_{M}} \right) + \frac{M_{M}^{2}\delta_{M}}{D_{M}}}}\frac{1}{D_{M}}} \\{\frac{E_{M\quad B} - {\frac{M_{M}\delta_{M}}{D_{M}}\left( {T_{M} + T_{M\quad F}} \right)}}{\left( {R_{L} + R_{M}} \right) + \frac{M_{M}^{2}\delta_{M}}{D_{M}}}R_{L}} \\\frac{E_{M\quad B} - {\frac{M_{M}\delta_{M}}{D_{M}}\left( {T_{M} + T_{M\quad F}} \right)}}{\left( {R_{L} + R_{M}} \right) + \frac{M_{M}^{2}\delta_{M}}{D_{M}}}\end{bmatrix}\quad\lbrack 1\rbrack}} & {{Eq}.\quad (20)}\end{matrix}$

[0178] The above-mentioned test models can be equivalently modeled bysetting the following values to the characteristic or the input statevalue of the steady load test model shown in FIG. 9:

[0179] {circle over (1)} The idle test model becomes equivalent byrendering the input torque T_(M)=0.

[0180] {circle over (2)} The lock test model becomes equivalent byrendering a viscous damping coefficient and the brush voltage droprespectively D_(M)=∞, E_(MB)=0.

[0181] {circle over (3)} The induced voltage test model becomesequivalent by rendering the motor current and the brush voltage droprespectively I_(M)=0, E_(MB)=0.

[0182] {circle over (4)} The braking test model becomes equivalent byrendering the internal electromotive force of the battery E₀=0, and byrendering the internal resistance R_(B) the braking resistance R_(L).

[0183] 2. Identification of Steady State

[0184]FIG. 14 shows an identification process 2 of the steady internalcharacteristic value included by the steady functional model having beendescribed referring to FIG. 1. Since the steady internal characteristicvalue known beforehand from a design value, the characteristic value ofa similar article, the measured value of the characteristic, and thelike can be determined as a fixed value in the steady identificationprocess (process 26), remaining unfixed values can be identified(process 27).

[0185] As for an analysis technique applied to this, the governmentequation of the steady test model is converted into a statistical modelsuch as a canonical correlation analysis or a multiple fold recurrenceanalysis (simple recurrence analysis), determines a deviated recurrencecoefficient from the steady test data, and derives the steady internalcharacteristic value from the relationship between the deviatedrecurrence coefficient and the government equation (process 28). Also,as for the steady characteristic formed by many factors, the factors areanalyzed, and divided into factors of which quantification is possibleand factors of which quantification is difficult by modeling the formingprocess of the steady internal characteristic value with a theoreticalequation, an experimental equation, and the like, thereby identifyingthe factors of which quantification is difficult (process 29).

[0186] 2.1 Test Plan of Steady Identification

[0187] In the steady functional model shown in FIG. 8, there are sixkinds of the steady internal characteristic values M_(M), R_(M), D_(M),δ_(M), T_(MF), and E_(MB) which form the subject of the steadyidentification. In order to determine these steady internalcharacteristic values, it is necessary to firstly clarify the functionof the steady model in FIG. 8 and the characteristic of the steadyinternal characteristic values, and to clarify the relationship with thetest method.

[0188] 2.1.1 Verification of Known/measurable Steady InternalCharacteristic Value (Process 26)

[0189] Firstly, known characteristic values and the characteristicvalues which can be solely measured are clarified in the steady internalcharacteristic values included in the steady functional model. Among thecharacteristic values which govern the steady state of a general productor part, there are a large number of characteristic values which can beclarified without performing the steady test such as a characteristicvalue clarified in the literature or the like, a characteristic valuewhich can directly utilize the design value, and a characteristic valuewhich can be directly measured. Accordingly, it is important todetermine the characteristic values by a preliminary investigation ormeasurement in case of the steady identification.

[0190] Reviewing the motor used here, such characteristic values to bedetermined are a brush voltage drop E_(MB), an insulation resistanceR_(C) omitted at the time of modeling the steady functional model inFIG. 8, a motor winding resistance R_(M), and the like. These steadycharacteristics are given as follows:

[0191] {circle over (1)} Since being a voltage drop due to a graphitebrush, the brush voltage drop E_(MB) assumes constant regardless of thecurrent I_(M) during the motor rotation, and assumes a minute DCresistance in the stop state. Accordingly, 0.7 [V] shown in theliterature is applied during the motor rotation, and the direct currentin the stop state can be neglected.

[0192] {circle over (2)} The insulation resistance R_(C) assumes thevalue [MΩ] which does not influence the result.

[0193] {circle over (3)} The winding resistance R_(M) of the motor canbe determined by the ohmmeter or by calculating the motor voltage andcurrent in the motor lock state shown in FIG. 11.

[0194] 2.1.2 Steady Functional Model and Statistical Model (Process 28)

[0195] There is a multivariate analysis such as a multiple foldrecurrence analysis and a simple recurrence analysis in the analysistechnique frequently used for the product development. The recurrenceequation of the multiple fold recurrence model applied thereto is givenas follows:

y=b ₀ +b ₁ x ₁ +b ₂ x ₂ . . . b _(n) x _(n) +e _(rr)  Eq.(21)

[0196] Applying the multiple fold recurrence equation shown in equation(21) to the steady functional model including a plurality of unknownsteady internal characteristic values will be examined. As for themethod of application, dependent variables and independent variables inthe steady government Eq.(8) are made correspond to an objectivevariable “y” and predictor variables x₁−x_(n), to be determined from thesteady test data.

[0197] Also, deviated recurrence coefficients b₀−b_(n), are valuescombining known and unknown steady internal characteristic values.Accordingly, unknown steady internal characteristic values can bederived by relating (associating) the deviated recurrence coefficientsto (with) the steady internal characteristic values. In Eq.(21), e_(rr)is a residual. Also, when the multiple fold recurrence equation has aplurality of different objective variables, these are expressed bysimultaneous equations so that the canonical correlation analysis isapplied. As a matter of course, the test data obtained by a plurality ofdifferent test methods, the deviated recurrence coefficient, and thesteady internal characteristic value of the recurrence model can berelated to each other.

[0198] In the multiple fold recurrence analysis of the motor, the steadyinternal characteristic value can be clarified by the following processfrom the data of the steady load test shown in FIG. 9:

[0199] Firstly, as for the multiple fold recurrence model, the followingmultiple fold recurrence equations can be derived from the governmentEq.(13) of the steady load test model in FIG. 9: $\begin{matrix}{T_{M} = {{- T_{M\quad F}} - {D_{M}\omega_{M}} + {M_{M}I_{M}}}} & {{Eq}.\quad (22)} \\{T_{M} = {{- \left( {T_{M\quad F} + \frac{{D_{M}E_{0}} - {D_{M}E_{M\quad B}}}{M_{M}\delta_{M}}} \right)} + {\left( {\frac{R_{M}D_{M}}{M_{M}\delta_{M}} + M_{M}} \right)I_{M}}}} & {E\quad {q.\quad (23)}}\end{matrix}$

[0200] It is to be noted that equation (22) corresponds to the 1st rowof Eq.(13), and equation (23) corresponds to the 2nd row of Eq.(13).Since the internal resistance R_(B) of the battery is very small, andthe internal electromotive force E₀ of the battery and a motor outputvoltage V_(M) are considered to be approximately equal, E₀ in Eq.(23) issubstituted for the dependent variable V_(M) (motor output voltage) ofEq.(13).

[0201] The recurrence equation with the torque T_(M) of these equationsbeing made an objective variable and the motor current I_(M) and theangular velocity ω_(M) being made predictor variables is given asfollows (process 281), where the upper equation of the followingequations (24) forms a multiple fold recurrence equation with twopredictor variables, and the lower equation forms a simple recurrenceequation with a single predictor variable: $\begin{matrix}\left. \begin{matrix}{\quad {T_{M} = {B_{10} + {B_{11}I_{M}} + {B_{12}\omega_{M}}}}} \\{\quad {T_{M} = {B_{20} + {B_{21}I_{M}}}}}\end{matrix} \right\} & {{Eq}.\quad (24)}\end{matrix}$

[0202] Also, constants (intercepts) B₁₀, B₂₀, and deviated recurrencecoefficients B₁₁, B₁₂, B₂₁ can be expressed by the following equations:$\begin{matrix}\left. \begin{matrix}{\quad {B_{10} = {- T_{M\quad F}}}} \\{\quad {B_{11} = {- D_{M}}}} \\{\quad {B_{12} = M_{M}}} \\{\quad {B_{20} = {{{- D_{M}}\frac{E_{0} - E_{M\quad B}}{M_{M}\delta_{M}}} - T_{M\quad F}}}} \\{\quad {B_{21} = {M_{M} + \frac{R_{M}D_{M}}{M_{M}\delta_{M}}}}}\end{matrix} \right\} & {{Eq}.\quad (25)}\end{matrix}$

[0203] The steady internal characteristic values derived from thedeviated recurrence coefficients shown in equations (25) are given asfollows (process 282):

[0204] Firstly, T_(MF), D_(M), M_(M) correspond to the respectivedeviated recurrence coefficients from the upper four rows of Eqs. (25).$\begin{matrix}\left. \begin{matrix}{\quad {T_{M\quad F} = {- B_{10}}}} \\{\quad {D_{M} = {- B_{11}}}} \\{\quad {M_{M} = B_{12}}}\end{matrix} \right\} & {{Eq}.\quad (26)}\end{matrix}$

[0205] Then, δ_(M) can be derived by substituting equation (26) for fourrows of Eqs. (25) as follows: $\begin{matrix}{\delta_{M} = {\frac{B_{11}}{\left( {B_{20} - B_{10}} \right)B_{12}}\left( {E_{0} - E_{M\quad B}} \right)}} & {{Eq}.\quad (27)}\end{matrix}$

[0206] It is to be noted that the induced voltage E₀ of the battery andthe brush voltage drop E_(MB) in equation (27) are assumed to be knownsteady internal characteristic values.

[0207] Moreover, R_(M) can be derived from the following equation bysubstituting Eqs. (26) and (27) for five rows of Eqs. (25):$\begin{matrix}{R_{M} = {\frac{B_{12} - B_{21}}{B_{20} - B_{10}}\left( {E_{0} - E_{M\quad B}} \right)}} & {{Eq}.\quad (28)}\end{matrix}$

[0208] While in the above-mentioned example, all of the steady internalcharacteristic values have been determined from the government Eq.(8) bythe recurrence analysis, in many cases of the actual steadyidentification, the steady internal characteristic values are derived bycombining a plurality of test methods. For example, in theabove-mentioned example, as for the 3rd row of Eqs.(26) deriving themotor constant M_(M) and equation (28) determining the windingresistance RM, both M_(M) and R_(M) can be determined from Eq.(17) ofthe lock test model and the test data as well.

[0209] 2.1.3 Factor Analysis of Steady Characteristic (Process 29)

[0210] The steady internal characteristic value of the steady functionalmodel includes many internal factors forming this characteristic.Accordingly, it is necessary to clarify the factors composing eachcharacteristic value by performing a factor analysis, and to formulatethe result. Also, it is necessary to divide the factors into knownfactors and unknown factors based on the result, and to perform a steadyidentification for clarifying an influence degree of the unknown oruncertain factors by the model or the test data.

[0211] To the steady identification method, a sensitivity analysis orthe like is effective by which the relationship between the variance ofthe government factor and the steady internal characteristic value istaken as a sensitivity. Particularly, in case the steady internalcharacteristic value has a non-linear characteristic influenced byanother state value, the sensitivity analysis is important.

[0212] If the motor constant is considered, for example, P, “a”, and Zare known values in Eq.(1) since they are design constants determined bythe motor structure. However, it is difficult to determine all of themagnetic fluxes φ per pole since it is distributed in the gap betweenthe field and the armature. Accordingly, to identify the motor constantmeans to identify the magnetic flux φ by the test data. All of themagnetic fluxes φ per pole determined from the identified motor constantM_(M) are given as follows: $\begin{matrix}{\varphi = {M_{M}\frac{2\pi \quad a}{P\quad Z} \times 10^{8}}} & {{Eq}.\quad (29)}\end{matrix}$

[0213] Moreover, as a brief example, the winding resistance R_(M) of themotor is given by the following equation determined by a line length“1”, a sectional area “S”, a specific resistance “ρ”, and a temperaturecoefficient a “α”, being governed by a temperature “t”: $\begin{matrix}{R_{M} = {\rho \quad \frac{l}{S}\left( {1 + {\alpha \quad t}} \right)}} & {{Eq}.\quad (30)}\end{matrix}$

[0214] From equation (30), 1, S, ρ, and α assume the design valuesdetermined by the structure and the stuff, while the temperature “t” isgoverned by the temperature of the test environment and the temperaturerise by self-heating of the motor. Accordingly in the test forperforming the identification, the temperature of the motor forms animportant factor which governs the characteristic value. Also, upon theexamination of the high temperature environment and low temperatureenvironment, it is required to perform a temperature correction, byEq.(30), to R_(M) in the government Eq.(3) and the steady governmentequations of equation (8) and subsequent equations.

[0215] 2.1.4 Motor Seady Characteristic

[0216] The steady internal characteristic value of the motor isrepresented by a torque-current characteristic representing therelationship between the output torque T_(M) [Nm] and the motor currentI_(M) [A], and by a torque-velocity characteristic representing therelationship with the angular velocity ω_(M) [rad/sec], based on theresult measured in the above-mentioned steady load test (FIG. 9).Firstly, the torque-velocity characteristic can be expressed by thefollowing equation obtained by substituting the motor current I_(M) ofEqs.(22) and (23), the latter itself representing the torque-currentcharacteristic: $\begin{matrix}{T_{M} = {\left( {{\frac{M_{M}}{R_{M}D_{M}}\left( {E_{0} - E_{M\quad B}} \right)} - T_{M\quad F}} \right) - {\left( {D_{M} + \frac{M_{M}^{2}\delta_{M}}{R_{M}}} \right)\omega_{M}}}} & {{Eq}.\quad (31)}\end{matrix}$

[0217] If the torque-current characteristic and the torque-velocitycharacteristic of the motor are available from a catalog, technicalmaterials, or the like, the steady internal characteristic value may bedetermined from the characteristic diagram. This motor characteristic isshown in FIG. 15.

[0218] The relationship between the two recurrence lines in FIG. 15 isexpressed by Eq.(22). In FIG. 15, the intersection of X axis and thetorque-angular velocity characteristic shows the motor lock state, andthe intersection of Y axis and the torque-angular velocitycharacteristic shows the idle state. Accordingly, as for the motorconstant M_(M) and the winding resistance R_(M),their approximate valuescan be determined by substituting the motor current I_(M), the torqueT_(M), and the voltage V_(M) in the lock state for Eq.(17). Also, bysubstituting the result for Eq.(22), an approximate value of the viscousresistance coefficient δ_(M) at the time of idling can be determined,whereas the friction torque T_(MF) is neglected.

[0219] 2.2 Motor Steady Test and Steady Identification Result

[0220] The characteristic value in the steady state is identified fromthe motor functional model shown in FIG. 4(1). It is to be noted thatthe motor used in this identification is a small DC motor of aseparately excited type widely used for an electric actuator of a car,and a permanent magnet is used for the field.

[0221] The steady test for the steady identification has been performedto the idle test, the lock test, and the steady load test. The idle testand the lock test have been performed as a test under the specifiedconditions of the motor. The former has been performed with the outputaxis of the motor being released in the state of the input torqueT_(M)=0, and the latter has been performed with the output axis beingfixed in the state of the output angular velocity ω_(M)=0.

[0222] Moreover, in the steady load test, a string hung with a weighthas been winded onto a drum attached to the motor output axis, wherebythe load torque has been calculated from the mass of the weight and theradius of the drum. Also, the test conditions are shown in the followingTable 3 together with the result of the steady identification.

[0223]FIG. 17 shows a result of the steady test performed to the motorof FIG. 16 and the recurrence equations, where X and Y axes of FIG. 15are exchanged.

[0224]FIG. 17 is a diagram showing the measured values of thetorque-current characteristic representing the relationship between theoutput torque T_(M) [Nm] in the steady state and the motor current I_(M)[A], and representing the relationship with the angular velocity ω_(M)[rad/sec], and its recurrence equation. The characteristic value on theX axis represents the motor idle state (T_(M)=0) as mentioned above, andthe intersection between the torque-velocity characteristic and the Yaxis shows the lock state or the starting moment of the motor with theangular velocity ω_(M)=0. The points shown by variable names in FIG. 17show a basic characteristic and a rating characteristic (dotted line) inTable 1. Also, the black circle in FIG. 17 shows the measured values.

[0225] Moreover, the basic performance at the time of starting andidling obtained in FIG. 17 is shown in Table 1, and a catalogspecification published by a motor maker is shown in Table 2 asreference data. TABLE 1 CHARACTERISTIC NAME SYMBOL UNIT CHARACTERISTICVALUE MOTOR IDLE CURRENT I_(M) _(—O) [A] 0.1 IDLE ANGULAR VELOCITY ω_(M)_(—O) [rad/sec] 1410. 0 STARTING TORQUE T_(M) _(—L) [Nm] 28.0 × 10⁻³STARTING CURRENT I_(M) _(—L) [A] 4.0 TEST POWER INTERNAL ELECTROMOTIVEE_(B) [V] 12.0 SUPPLY FORCE POWER SUPPLY INTERNAL R_(B) [A] 1.0 × 10⁻³RESISTANCE

[0226] TABLE 2 CHARACTERISTIC NAME SYMBOL UNIT CHARACTERISTIC VALUECONDITION RATED CURRENT I_(M) _(—S) [A] 0.57 MAX 0.9 RATED ANGULARVELOCITY ω_(M) _(—S) [rad/sec] 1210.0 TOLERANCE ± 160.0 RATED LOAD T_(M)_(—S) [Nm] 4.0 × 10⁻³ RATED VOLTAGE V_(M) [V] 12.0

[0227] Table 3 shows a result of the steady identification by the testdata of FIG. 17. TABLE 3 CHARACTERISTIC NAME SYMBOL UNIT CHARACTERISTICVALUE MOTOR STATE VISCOUS RESISTANCE D_(M) [Nm/(rad/sec)] 0.65 × 10⁻⁶CHARACTERISTIC COEFFICIENT KINETIC FRICTION TORQUE T_(MP) [Nm] 0.1 ×10⁻⁶ WINDING RESISTANCE R_(M) [Ω] 3.0 BRUSH VOLTAGE E_(BR) [V] 0.7INSULATION RESISTANCE R_(C) [MΩ] 10.0 MOTOR CONSTANT M_(M) [Nm/A] 7.25 ×10⁻³ VELOCITY COEFFICIENT CORRECTION δ_(M) 1.0 TEST CONDITION TESTVOLTAGE E_(O) [V] 12.0 (KNOWN) INTERNAL RESISTANCE R_(B) [Ω] 50.0 × 10⁻³AMBIENT TEMPERATURE t_(F) [deg] 25.0

[0228] Table 3 is a result assembled according to the process of thesteady identification examined so far. Among the identified steadyinternal characteristic values, E_(BR) has been assumed to be aprescribed value determined from the literature. Also, R_(C) is ameasurement result of the insulation testing set. The other steadyinternal characteristic values M_(M), R_(M), D_(M), δ_(M), and T_(MF)are determined from the multiple fold recurrence analysis and the simplerecurrence analysis by the above-mentioned Eqs.(24)-(27). Also, sincethe internal resistance R_(B) of the battery is so small that theresistance value of the current measurement distributary unit has beenapplied to the internal resistance R_(B) of the battery.

[0229] It is to be noted that in order to determine five coefficientsB₁₀, B₂₀, B₁₁, B₁₂, and B₂₁, five simultaneous equations are required.T_(M), I_(M), and ω_(M) are vectors where equal to or more than fivevalues determined by the characteristic of FIG. 17 exist.

[0230]FIG. 18 shows a simulation result of the thus identified steadyinternal characteristic values by using the steady government equationobtained by substituting Eq.(4) of the battery model for Eq.(8) of themotor steady state.

[0231] Thick solid lines in FIG. 18 show results of the steady state ofthe angular velocity ω_(M) and the motor current I_(M), while thindotted lines show simulation results performed in Eq.(5) by using thetransient internal characteristic value before identification describedlater. It is seen from FIG. 18 that the results of the steady statecoincides with each other in the steady region where the transient statebecomes stable.

[0232] 3. Identification of Transient State (Process 32)

[0233] Since the steady internal characteristic values of the motorshown in the above-mentioned Table 3 are clarified, the moment ofinertia J_(M) and the inductance L_(M) as transient internalcharacteristic values governing the transient function of the motor areidentified next with the measured value of the motor current I_(M), bythe transient identification process 32 shown in FIG. 19.

[0234] As an identification method, approximate values are firstlyprovided to the transient internal characteristic values to perform asimulation so that initial values are determined (process 36), then thetime history sensitivities of the transient internal characteristicvalues for the motor current I_(M) are determined (process 38), and thedeviation (process 37) between the measured value (process 35) of themotor current I_(M) and the simulation value assumes the least for thesensitivity indexed.

[0235] Moreover, the viscous resistance coefficient δ_(M) and thekinetic friction torque F_(MF) already identified are again identifiedin the transient identification for the confirmation. As for theidentification, the assembly/disassembly and the replacement by a blockreplacement method can be performed to the model of the operation systemand the load connected to the motor source supply system and therotation system.

[0236] 3.1 Conversion from Functional Model into Transient Test Model(Process 33)

[0237] The test model in the transient state reproduces an actualitytest providing a specified condition to the input/output system of thetested object in the same way as the steady identification by thefunctional model and its government equation. In the transientidentification, the test data obtained by providing the specifiedcondition to the tested object, and the simulation result by the testmodel are related, so that the characteristic value group included inthe government equation is identified. Also, the test model of thetransient identification models the drive or the load to be incorporatedby the following method:

[0238] {circle over (1)} Input/output model for driving

[0239] The input/output model for driving is a model for supplyingdriving energy such as a power supply and a torque in order to drive thetested object. This model occasionally has an operation system or acontrol system for operating the supplying method of the driving energy.For example, the system connected to the electric system of the motorcorresponds to such a system including a battery of a power supply, anoperation switch of a motor, a control unit, and the like. As a specificexample, there is a braking resistance of the regenerative brakingmentioned above, or the like.

[0240] {circle over (2)} Input/output model for load

[0241] The input/output model for the load is a model for accumulatingor consuming the energy such as a characteristic which forms the load ofthe tested object and an actual load. This model occasionally has theoperation system or the control system for operating how to provide theload energy. In the rotation system or the mechanical system of themotor, there is a method for connecting a stiffness (spring stiffness),the moment of inertia, the viscous resistance coefficient, and the like.

[0242] It is to be noted that in case the characteristic whose value islarge is connected, it can be considered that the output axis is fixed.Also, as an expediency for providing the fixed load, there is a methodby which the current, the torque, and the like are provided as anadditional load, and the voltage and the angular velocity which become apair are made an observation quantity. For operating a machine load, theload is switched over by simultaneously using models of a clutch, abrake, and the like.

[0243] 3.1.1 Actual Load Transient Test Model of Motor (Process 34)

[0244] In the transient test model which reproduces the state of thepractical use, the battery model in FIG. 6 is united with the electricsystem of the motor model in FIG. 4 (1), and an actual load model or anequivalent load model is further united with the rotation system to beconverted into a model for an execution closed from the outside.

[0245] (1) Actual load model of electrically driven arm mechanism

[0246]FIG. 20 shows a load model of an electrically driven arm mechanismhaving an operation arm whose operation range is restricted by θ_(max)at a rack side of a worm gear, as an example of a practical load.

[0247]FIG. 21 shows a functional model for an execution in which theelectrically driven arm mechanism in FIG. 20 is incorporated in thebattery and the motor model. The electrically driven arm mechanism inFIG. 21 has a stiffness K_(C) of a stopper, a reduction ratio N_(G) of areduction unit, and a contact radius L_(C) of the stopper. Inside theelectrically driven arm mechanism, there are a presumed rotation angle{circumflex over (θ)}_(R) of the arm and an internal state x_(C).

[0248] Also, the condition judgment shown by a hexagon frame in FIG. 21is a model which judges an arm operation range, and operates a switchvariable S_(WC). This functional model has an additional load of torqueT_(L) as an external load.

[0249] The government equations of the electrically driven arm mechanismare given as follows: $\begin{matrix}{{\begin{bmatrix}0 \\T_{M} \\\omega_{L}\end{bmatrix} = {\begin{bmatrix}{- \frac{1}{K_{C}L_{C}^{2}S_{W\quad C}}} & 0 & N_{G} & 0 \\0 & {- N_{G}} & 0 & {{- N_{G}}T_{L}} \\0 & 0 & N_{G} & 0\end{bmatrix}\quad\begin{bmatrix}{\overset{.}{x}}_{C} \\x_{C} \\\omega_{M} \\1\end{bmatrix}}}{{\hat{\theta}}_{L} = {N_{G}{\int{\int{{\overset{.}{x}}_{M}{t}}}}}}} & {{Eq}.\quad (32)}\end{matrix}$

[0250] In this government equations (32), the upper equation indicatesthe government equation of the electrically driven arm mechanism shownon the right side of FIG. 21, and the lower equation indicates thepresumed rotation angle {circumflex over (θ)}_(L) of the arm. Also, themodel determining the rotation range of the arm is given as follows:

if({circumflex over (θ)}_(R)≧θ_(max)) then (S _(WC)=1) else (S_(WC)=0)  Eq.(33)

[0251] Equation (33) is for a condition judgment S_(WS) in whichS_(WC)=0 when the presumed rotation angle {circumflex over (θ)}_(R) ofthe arm lies within the rotation range θ_(max), and otherwise S_(WC)=1.

[0252] If the above-mentioned Eq.(5) where the battery and motor areintegrated, and the government Eq.(32) of the electrically driven armmechanism are integrated, the following equation can be obtained:$\begin{matrix}{\begin{bmatrix}0 \\0 \\0 \\\omega_{L} \\\omega_{M} \\V_{P} \\I_{M} \\T_{M}\end{bmatrix} = {\quad{\left\lbrack \begin{matrix}{- J_{M}} & 0 & 0 & {- D_{M}} & M_{M} & {- N_{G}} & {{- T_{M\quad F}} - {N_{G}T_{L}}} \\0 & {- L_{M}} & 0 & {{- \delta_{M}}M_{M}} & {{- R_{M}} - {R_{C}R_{B}Y_{0}}} & 0 & {{R_{C}I_{0}} - E_{M\quad B}} \\0 & 0 & {- \frac{1}{K_{C}L_{C}^{2}S_{W\quad C}}} & N_{G} & 0 & 0 & 0 \\0 & 0 & 0 & N_{G} & 0 & 0 & 0 \\0 & 0 & 0 & 1 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & {{- R_{C}}R_{B}Y_{0}} & 0 & {R_{C}I_{0}} \\0 & 0 & 0 & 0 & {R_{C}Y_{0}} & 0 & I_{0} \\0 & 0 & 0 & 0 & 0 & N_{G} & {N_{G}T_{L}}\end{matrix}\quad \right\rbrack \quad \cdot {\quad\begin{bmatrix}{\overset{.}{x}}_{M} \\{\overset{.}{x}}_{L} \\{\overset{.}{x}}_{C} \\x_{M} \\x_{L} \\x_{C} \\1\end{bmatrix}}}}} & {{Eq}.\quad (34)}\end{matrix}$

[0253] In equation (34), the 1st-3rd rows form the state equations, andthe 4th and the following rows form output equations for observing theinternal transient state value. It is to be noted that Y₀ and I₀ in theequations are an equivalent admittance and a current source respectivelyshown in equations (6) and (7).

[0254] (2) Axis fixed model of motor

[0255] The model in which the output axis of the motor is fixed can beshown by next FIG. 22. This model can be replaced with the arm mechanismon the right side of the motor actual load transient test model in FIG.21.

[0256]FIG. 22 is a model in which the model of the electrically drivenarm mechanism in the actual load transient test model of FIG. 21 isreplaced with stiffness K_(C) which fixes the motor output axis. Themotor torque T_(MF) in FIG. 22 reproduces the lock torque in the statewhere the output axis of the motor is fixed. It is to be noted that thegovernment equation is omitted in this case.

[0257] 3.1.1.1 Fixed Torque Transient Test Model of Motor

[0258] There is a fixed torque test for providing a fixed torque as aload in the load test performed in the actual machine test. This testmodel unites the battery model in FIG. 6 with the motor model in FIG.4(1) to be converted into the model for execution closed from theoutside. The converted functional model for the execution is shown inFIG. 23.

[0259] In FIG. 23, by uniting the battery and the motor, the motorvoltage V_(M) and the current I_(M) form the observation quantities.Moreover, since the input torque T_(M) of the motor becomes the loadtorque generated inside the model, the input torque T_(M) becomes theadditional load T_(M), and the output angular velocity ω_(M) becomes theobservation quantity. It is to be noted that in the idle test of themotor, the additional load T_(M) can be made 0.

[0260] The government equation for execution of the function modeltherefor shown in FIG. 23 is given as follows: $\begin{matrix}{\begin{bmatrix}{\overset{.}{x}}_{M} \\{\overset{.}{x}}_{L} \\\omega_{M} \\V_{M} \\I_{M}\end{bmatrix} = {\begin{bmatrix}{- \frac{D_{M}}{J_{M}}} & \frac{M_{M}}{J_{M}} & {- \frac{T_{M} + T_{M\quad F}}{J_{M}}} \\{- \frac{\delta_{M}M_{M}}{L_{M}}} & {- \frac{R_{M} + {R_{C}R_{B}Y_{0}}}{L_{M}}} & \frac{{R_{C}I_{0}} - E_{M\quad B}}{L_{M}} \\1 & 0 & 0 \\0 & {{- R_{C}}R_{B}Y_{0}} & {R_{C}I_{0}} \\0 & {R_{C}Y_{0}S_{WA}} & I_{0}\end{bmatrix}*\begin{bmatrix}x_{M} \\x_{L} \\1\end{bmatrix}}} & {{Eq}.\quad (35)}\end{matrix}$

[0261] In equation (35), the 1st-2nd rows form the state equations, andthe 3rd-5th rows form the output equations. It is to be noted that theequivalent admittance Y₀ and the current source I₀ are given by theabove-mentioned Eqs.(6) and (7).

[0262] Moreover, the process by which Eq.(35) is derived from theabove-mentioned Eq.(5) is as follows:

[0263] Firstly, the 1st and 2nd columns including the moment of inertiaJ_(M) and the inductance L_(M) are eliminated, and the dependentvariables at the 1st and the 2nd rows are made {dot over (χ)}_(M) and{dot over (χ)}_(L) Then, the input torque T_(M) of the independentvariable in Eq.(5) can be substituted for the additional load at the 1strow the 3rd column in Eq.(5).

[0264] 3.1.1.2 Inertia Rotation Test Model of Motor

[0265] With the motor, the motor voltage V_(M), the current I_(M), theangular velocity ω_(M) of the rotation system, and the torque T_(M) canbe measured. Among them, the electric system particularly has acharacteristic that data with high accuracy are easily measured. Howeverin the functional model shown in FIG. 23, the motor voltage V_(M) nearlyequals the electromotive force E₀ of the battery, so that it can not beused as the data for identification.

[0266] However, as for the induced voltage V_(M) generated when themotor power supply is turned off, the test data for identification canbe measured by the inertial rotation test. The model of this inertialrotation test is shown in FIG. 24.

[0267]FIG. 24 shows a model of the inertial rotation test from themoment when the power supply of the motor in the idle state is turnedoff until the motor is stopped. It is to be noted that the insulationresistance R_(C) is omitted because the current is minute. The motormodel of the inertial rotation test shown in FIG. 24 is given asfollows: $\begin{matrix}{\begin{bmatrix}{\overset{.}{x}}_{M} \\{\overset{.}{x}}_{L} \\\omega_{M} \\V_{M} \\I_{M}\end{bmatrix} = {\begin{bmatrix}{- \frac{D_{M}}{J_{M}}} & \frac{M_{M}}{J_{M}} & {- \frac{T_{MF}}{J_{M}}} \\{- \frac{\delta_{M}M_{M}}{L_{M}}} & {- \frac{R_{M}}{L_{M}}} & {- \frac{E_{MB}}{L_{M}}} \\1 & 0 & 0 \\{{- \delta_{M}}M_{M}} & {- R_{M}} & {- E_{MB}} \\0 & 1 & 0\end{bmatrix}*\begin{bmatrix}x_{M} \\x_{L} \\1\end{bmatrix}}} & \text{Eq.~~(36)}\end{matrix}$

[0268] In equation (36), the 1st-2nd rows form the state equations, andthe 3rd-5th rows form the output equations. It is to be noted that thetransient test model of FIG. 23 is used for the example of the transientinternal characteristic value identification performed here, instead ofthe inertial rotation test model.

[0269] 3.1.1.3 Actual Load Transient Test Model of Motor havingOperation System

[0270] (1) Operation switch

[0271] The start, the stop, the regular rotation, the reversed rotationof the motor, and the basic operation of the electric brake can beperformed by a switch circuit shown in FIG. 25. The test model in whichthe modeled switch circuit is incorporated into the actual loadtransient test model of FIG. 21 will now be examined.

[0272] The motor operation model can be incorporated between the voltageV_(M) and the current I_(M) which connect the battery and the motor inthe motor actual load transient test model shown in FIG. 21. Byincorporating the operation switch, the actual load transient test modelin FIG. 21 can reproduce the start, the stop, the regular rotation, thereversed rotation of the motor, and the drive state of the electricbrake by the switch operation. It is to be noted that the simulationresults of the model will be described after the identification of thetransient state.

[0273]FIG. 25 is a circuit diagram of the operation switch incorporatedbetween the battery and the motor. The functional model of the switchcircuit is shown in the next FIG. 26.

[0274] The resistance R_(L) in FIG. 26 is the braking resistance for theelectric brake. Moreover, neither the contact resistance nor theinsulation resistance of the switch are considered.

[0275] The operation switch in FIG. 26 is composed of S_(WS) forstarting and stopping the motor, S_(WL) for operating theregular/reversed rotation, and S_(WB) for selectingnecessity/unnecessity of the electric brake. As for an operation signalfor operating the switches, ON/OFF of the switches are respectivelyrepresented by “1” and “0”. The functions of the switches are asfollows:

[0276] {circle over (1)} The switch S_(WS) supplies the power to themotor when S_(WS)=1, and stops the power supply when S_(WS)=0.

[0277] {circle over (2)} As for S_(WL) which controls theregular/reversed rotation, a switchover switch variable S_(WL) _(—) ₀assumes “1” when S_(WL)=0, so that the current I_(M) of the electriccircuit in FIG. 23 flows in the direction of the arrow to rotate themotor regularly. An SS_(WL) _(—) ₁ assumes “1” when S_(WL)=1, so thatthe current flows reversely to rotate the motor reversely. Also, S_(WL)_(—) ₀ and S_(WL) _(—) ₁ are the switchover switch variables prohibitingthat both of S_(WL) _(—) ₀ and SS_(WL) _(—) ₁ simultaneously assume thestate of “1” or “2”. On the functional model, this operation makesS_(WL) on the current side switch over the polarity of the outputcurrent I_(S), so that S_(WL) on the voltage side restores the inputvoltage V_(S) which has come back to the original polarity.

[0278] {circle over (3)} As for the braking by the electric brake, theelectric brake is made ON when S_(WB)=1 and OFF when S_(WB)=0 under thecondition of the power supply being made OFF, i.e. S_(WS)=0. Theelectric brake consumes the induced current caused by the motor idlingwhen the power supply is made OFF by the braking resistance R_(L) togenerate the braking torque. This braking torque is determined by thebraking resistance R_(L), so that the maximum braking torque isgenerated when the resistance value is 0 [Ω].

[0279] The government equation determined from FIG. 26 is given asfollows: $\begin{matrix}{\begin{bmatrix}I_{M} \\V_{B}\end{bmatrix} = {\begin{bmatrix}{\frac{1}{R_{L}}{S_{WB}\left( {1 - S_{wS}} \right)}} & S_{WE} \\{- S_{WE}} & 0\end{bmatrix}\begin{bmatrix}V_{M} \\I_{B}\end{bmatrix}}} & \text{Eq.~~(37)}\end{matrix}$

[0280] S_(WE) of equation (37) is a power supply switch variable forswitching over the motor start/stop, and regular/reverse direction ofthe current flow, which is expressed by the following equation:

S _(WE) =S _(WS)(S _(WL) _(—) ₀ −S _(WL) _(—) ₁)  Eq.(38)

[0281] The government equation of the power supply system obtained bymutually substituting the voltage V_(B) and the current I_(B) of thegovernment equations of the battery and the operation switch in Eqs.(4)and (37) to be integrated is given as follows: $\begin{matrix}{\left\lbrack I_{M} \right\rbrack = {\begin{bmatrix}{\frac{1}{R_{B}}S_{WA}} & {\frac{E_{0}}{R_{B}}S_{WE}}\end{bmatrix}\begin{bmatrix}V_{M} \\1\end{bmatrix}}} & \text{Eq.~~(39)}\end{matrix}$

[0282] S_(WA) in equation (39) is assumed to be a braking switchvariable by which ON/OFF of the power supply and a necessity/unnecessityof the electric brake are selected, and is expressed by the followingequation: $\begin{matrix}\left. \begin{matrix}{S_{WA} = {S_{WE}^{2} + {\frac{R_{B}}{R_{L}}{S_{WB}\left( {1 - S_{WS}} \right)}}}} \\{OR} \\{S_{WA} = {S_{WS} + {\frac{R_{B}}{R_{L}}{S_{WB}\left( {1 - S_{WS}} \right)}}}}\end{matrix} \right\} & \text{Eq.~~(40)}\end{matrix}$

[0283] In equation (40), as for S_(WE) ² in the upper equation, theswitchover switch variable member of the regular/reversed rotationassumes (S_(WL) _(—) ₀−S_(WL) _(—) )²=1, and S_(WS) ² assumes either “0”or “1” from equation (38). Therefore, the upper equation can beexpressed by the lower equation arranged by S_(WE) ²=S_(WS).

[0284] Finally, the government equation obtained by mutuallysubstituting the motor model of the actual load transient test model inEq.(32) and the voltage V_(M) and the current I_(M) of and the powersupply system in Eq.(39) to be integrated is expressed by the followingequation: $\begin{matrix}{\begin{bmatrix}0 \\0 \\0 \\\omega_{L} \\\omega_{M} \\V_{P} \\I_{S} \\T_{S}\end{bmatrix} = {\quad{\begin{bmatrix}{- J_{M}} & 0 & 0 & {- D_{M}} & M_{M} & {- N_{G}} & {{- T_{MF}} - {N_{G}T_{L}}} \\0 & {- L_{M}} & 0 & {{- \delta_{M}}M_{M}} & {- \left( {R_{M} + {R_{C}R_{B}Y_{0}}} \right)} & 0 & {{R_{C}I_{0}} - E_{MB}} \\0 & 0 & {- \frac{1}{K_{C}L_{C}^{2}S_{WC}}} & N_{G} & 0 & 0 & 0 \\0 & 0 & 0 & N_{G} & 0 & 0 & 0 \\0 & 0 & 0 & 1 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & {{- R_{C}}R_{B}Y_{0}} & 0 & {R_{C}I_{0}} \\0 & 0 & 0 & 0 & {R_{C}Y_{0}S_{WA}} & 0 & 0 \\0 & 0 & 0 & 0 & 0 & N_{G} & {N_{G}T_{L}}\end{bmatrix}*{\quad\begin{bmatrix}{\overset{.}{x}}_{M} \\\overset{.}{x_{L}} \\{\overset{.}{x}}_{C} \\x_{M} \\x_{L} \\x_{C} \\1\end{bmatrix}}}}} & \text{Eq.~~(41)}\end{matrix}$

[0285] In equation (41), the 1st-3rd rows form the state equations, andthe 4th and after rows form the output equations. The abstractedequivalent admittance Y₀ and the current source I₀ in Eq.(41) areexpressed by the following equations: $\begin{matrix}{Y_{0} = \frac{1}{R_{B} + {R_{C}S_{WA}}}} & \text{Eq.~~(42)} \\{I_{0} = {Y_{0}E_{0}S_{WE}}} & \text{Eq.~~(43)}\end{matrix}$

[0286] As an applied example of operating the motor by the electroniccircuit incorporated into a control unit or the like, it is possible toincorporate the electronic circuit into the actual load transient testmodel in FIG. 21 in the same way as the operation switch circuit of themotor in FIG. 25.

[0287] Also, it is possible to replace the operation system of the motorincluding the above-mentioned switch mechanism by the block replacementmethod. It is to be noted that while there is another method by whichthe electronic circuit is incorporated by using high circuit technology,the minimum function necessary for driving the motor is modeled here.Accordingly, the non-linear characteristic of a semiconductor, thetransient response of the electronic circuit and the like are excluded.

[0288] (2) Switching method

[0289] By the switching method, the ON/OFF ratio by which the electricpower is supplied at a cycle which does not influence the motor speed ischanged to perform a control. The switching method can be realized byperiodically switching over S_(WS) in FIG. 26. The electronic circuitdiagram close to this method is shown in FIG. 27 and its functionalmodel is shown in FIG. 28. FIGS. 27 and 28 respectively show a modelincluding the battery.

[0290] In FIG. 27, a signal voltage E_(CNT) of a rectangular waveapplied from the outside is converted into a base current by aresistance R_(A) of a transistor T_(R) to make the transistor T_(R)perform an ON/OFF operation. At this time, E₀ and R_(B) respectivelydenote a battery voltage and an internal resistance. D_(I) in FIG. 27denotes a wheeling diode, and has the following two functions: One is afunction of restoring the energy saved by the inductance of the motorwinding generated when the transistor is turned OFF to the motor, andanother is a function of guarding the transistor from the high voltageinduced by the inductance. It is to be noted that a mechanism model of aswitching signal generator is omitted.

[0291] In the switching operation of FIG. 28, a conductive state of ONis represented by a low resistance R_(ON) and an interrupted state ofOFF is represented by a high resistance R_(OFF), in which both areswitched over. Also, the function of the wheeling diode is representedby a condition judgment S_(WD) shown by a hexagon frame and a resistanceR_(D) of the conductive state. Namely, the condition judgment S_(WE)judges the voltage E_(CNT) of the signal generator with the detectedvoltage E_(TH), the conductive resistance R_(ON) is selected whenS_(WE)=1, and the interception resistance R_(OFF) is selected whenS_(WE)=0. Selection is performed by the switchover switch variablesS_(WL) _(—) ₁ and S_(WL) _(—) ₀.

[0292] Moreover, as for the accumulated energy of the inductance, therelease period is judged by the counter electromotive force of theinductance which has turned to be negative, so that the energy isrestored to the motor through the conductive resistance R_(D) of thewheeling diode. The functions of the braking resistance R_(L) shown inFIG. 26 and the resistance R_(D) in FIG. 28 appear to be the same at aglance. However, the former functions by a positive induced voltage ofthe motor, while the latter functions by a negative counterelectromotive force by the inductance. The government equation of theswitching method taking these points into account is given as follows:$\begin{matrix}{\begin{bmatrix}V_{B} \\I_{M}\end{bmatrix} = {\begin{bmatrix}{{R_{ON}S_{{WE}\quad \_ 1}} + {R_{OFF}S_{{WE}\quad \_ 0}}} & 1 \\1 & {{- R_{D}}S_{WD}}\end{bmatrix}\begin{bmatrix}I_{B} \\V_{M}\end{bmatrix}}} & {{Eq}.\quad (44)}\end{matrix}$

[0293] It is to be noted that the switching signal generator isexcluded.

[0294] (3) Fixed current control method

[0295] The electronic circuit for controlling the motor by continuouslychanging the motor current or voltage is as follows:

[0296] The basic function of the motor is to convert the given currentinto the drive torque by the torque coefficient χ_(T). Accordingly, whenan electronic control is performed to the motor, it is a basic functionof the fixed current control to freely set a current targeted and tomaintain the current. FIG. 29 shows one of control circuits formaintaining the output current I_(S) in proportion to the value of adesignated current I_(CNT).

[0297] In FIG. 29, a triangle shows an operational amplifier or the likewhich has a high amplification ratio inside. The current I_(CNT)targeted is converted into the voltage by the resistance R_(S) to beinputted to + side of the amplifier. Also, the controlled current I_(M)is converted into the voltage by the current detecting resistance R_(E)to be returned to − side of the amplifier, thereby performing a feedbackcontrol. It is to be noted that E₀ and R_(B) are respectively theinduced voltage and the internal resistance of the battery.

[0298] Additional brief examination of the fixed current control will bedescribed as follows:

[0299] The voltage applied to + and − sides of the amplifier arerespectively V_(A) _(—) _(INP1)=R_(S)I_(CNT) and V_(A) _(—)_(INP2)=R_(E)I_(M), as seen from FIG. 29. Also, assuming theamplification ratio of the amplifier is A_(MP), the output voltage V_(A)_(—) _(OUT) is given as follows: $\begin{matrix}\begin{matrix}{V_{A\quad \_ \quad {OUT}} = \quad {\left( {{R_{S}I_{CNT}} - {R_{E}I_{M}}} \right)A_{MP}}} \\{= \quad {V_{M} + {R_{E}I_{M}}}}\end{matrix} & \text{Eq.~~(45)}\end{matrix}$

[0300] Also, the amplifier in FIG. 29 has a high amplification ratio.Therefore, if equation (45) is arranged assuming 1/A_(MP)=0, theequation of the output current I_(M) is given as follows, whereR_(S)/R_(E) in the equation indicates the current amplification ratio:$\begin{matrix}{I_{M} \cong {\frac{R_{S}}{R_{E}}I_{CNT}}} & \text{Eq.~~(46)}\end{matrix}$

[0301] V_(A) _(—) _(OUT) of Eq.(45) cannot generate the voltage equal toor more than a supply voltage V_(B) shown in FIG. 29. Also, a voltagedifference of an offset voltage V_(OFS) is necessary between an outputvoltage upper limit value of the amplifier and a power supply voltage.This relationship is expressed by the following inequality:

V _(A) _(—) _(OUT) ≦V _(L) −V _(OFS)  Eq.(47)

[0302] The output voltage V_(A) _(—) _(OUT) of the inequality (47)restricts the upper limit value of the output current I_(M), indicatingthe limit of the fixed current control. The output current I_(SL)exceeding this limit is determined by the closed circuit of the voltageof the battery induced voltage E₀ and the offset voltage E_(OFS) shownin FIG. 29, as well as the voltage drop by the motor winding resistanceR_(M), the current detecting resistance R_(E), and the battery internalresistance R_(B). However, since E₀ is not included in the functionalelement of the fixed current control, it is necessary to replace E₀ withthe supply voltage V_(B) which can be added to the functional element.The current I_(SL) considering this point is expressed by the followingequation: $\begin{matrix}{I_{SL} \cong \frac{V_{B} - E_{OFS}}{R_{M} + R_{E}}} & \text{Eq.~~(48)}\end{matrix}$

[0303] The current consumed by the internal circuit of the amplifier isvery low. Therefore, by neglecting the current, equation (48) turns intothe approximate equation of the current I_(B) supplied by the battery.

[0304] From the above-mentioned description, the fixed current controlcircuit in FIG. 29 has a function of switching over the controlledcurrent I_(M) given in equation (46) to the uncontrolled current I_(SL)given in Eq.(48) to be supplied. The functional model of the fixedcurrent control in which the above-mentioned contents are arranged andmodeled can be shown in FIG. 30.

[0305] Firstly, the government equation of the fixed current control canbe obtained from FIG. 30 as follows: $\begin{matrix}{\begin{bmatrix}I_{M} \\V_{B}\end{bmatrix} = {\begin{bmatrix}0 & S_{\quad {{WA}\quad M\quad P\quad \_ 1}} & {\frac{R_{S}}{R_{E}}S_{\quad {{WA}\quad M\quad P\quad \_ 0}}I_{C\quad N\quad T}} \\1 & {R_{E}S_{\quad {{WA}\quad M\quad P\quad \_ 1}}} & {\frac{R_{E}R_{S}}{R_{E}}S_{\quad {{WA}\quad M\quad P\quad \_ 0}}I_{C\quad N\quad T}}\end{bmatrix}\quad\begin{bmatrix}V_{M} \\I_{B} \\1\end{bmatrix}}} & {{Eq}.\quad (49)}\end{matrix}$

[0306] In equation (49), S_(WAMP) _(—) ₁ and S_(WAMP) _(—) ₀ indicateswitchover elements with the condition judgment for judging the limit ofthe fixed current control.

[0307] Finally, as for the fixed current control provides an in-controlwhile the current I_(B) within the control range of Eq.(46) is equal toor less than the current I_(SL) exceeding the control limit of Eq.(48),and a non-control when the current I_(B) exceeds the current I_(SL).This condition judgment S_(WAMP) provides the following equation. It isto be noted that Î_(B) and I_(SL) in equation (50) are estimated statevalues.

if (Î _(B) <I _(SL)) then S_(WAMP)=1 elkse S _(WAMP)=0  Eq.(50)

[0308] In Eq.(50), S_(WAMP)=1 means that the condition is fulfilled sothat the current is directly supplied from the battery, while S_(WAMP)=0means that the condition is not fulfilled so that the fixed currentcontrol is performed.

[0309] 3.2 Determination of Initial Characteristic Value

[0310] Characteristics which have not yet been clarified are mainlytransient characteristic values inside the motor. In order to understandthe relationship between the motor function and the functional model,the characteristic value is initialized by providing the design valueshown in the following Table 4 or an initial characteristic valueobtained from a similar part, by applying a steplike voltage to themotor in the idle state, and by sequentially updating the characteristicvalue experimentally. It is a matter of course that the process andmethod performed here can be simulated by the computer. TABLE 4CHARACTERISTIC NAME SYMBOL UNIT CHARACTERISTIC VALUE INERTIA MOMENTJ_(M) [Nm/(rad/sec²)] 0.4 × 10⁻⁶ INDUCTANCE L_(M) [H] 3.0 × 10⁻³

[0311] Upon the identification, the simulation results at the time whenthe functional model, corresponding to model when T_(M)=0 in FIG. 23, inthe idle state is initialized by providing the characteristic shown inthe Table 4 to Eq.(35) are shown in FIGS. 31(1) and 31(2). It is to benoted that for the steady internal characteristic value and the testcondition except those shown in the Table 4, the above-mentionedidentification results of the steady state shown in the Table 3 areused. Also, since the additional load T_(M) is idle, it is made “0”.

[0312]FIG. 31(1) shows the motor current I_(M) [A] and the angularvelocity ω_(M) [rad/sec]. In FIG. 31(1), a thin dotted line indicatesnatural data of a measured current, a thick dotted line indicates acurrent I_(M) _(—) _(MEG) [A] where signal processing is performed to ameasured result described later with a digital filter, a thick solidline indicates a current I_(M) _(—SIM) [A] of the simulation result, anda thin solid line indicates an angular velocity ω_(M) [rad/sec] of themotor.

[0313] Also, in FIG. 31(2), a thick solid line indicates a currentdeviation ΔI_(ERR) [A] of an initial value obtained by subtracting thecurrent I_(M) _(—) _(MEG) after being signal-processed with the filterfrom the current I_(M) _(—) _(SIM) determined by the simulation, and athin dotted line indicates a current deviation ΔI_(p) _(—err) [A] at thetime when a moment of inertia J_(M) described later is identified.

[0314] In FIG. 31(2), the current I_(M) shows a rise immediately afterthe power supply is turned ON where a high starting current flows, anddecreases with a lapse of time, being stabilized to the idle current.Oppositely, the angular velocity ω_(M) rises with a lapse of time tostabilize in the idle rotation number. The change of the motor currentindicates that the induced voltage of the motor is governed by theangular velocity ω_(M) through the motor constant M_(M).

[0315] 3.2.1 Noise Process of Measured Current

[0316] The motor current I_(M) is measured by connecting a distributaryresistance R_(S) of 50 [mΩ] between the motor and the power supply. Thestarting current measured at this time is shown by a thin dotted linewhich varies extensively in FIG. 31(1). The extensive variance indicatedby the actually measured value is due to the brush and a commutator ofthe motor, and is remarkably shown in a low rotation region immediatelyafter the start. Particularly, the extensive variance which occurs atthe rise of the rotation depends on a rotation angle of the commutatorfor the brush, which is remarkably shown in a small motor having a fewcontact pieces of commutator.

[0317] The result of performing the signal processing to the measureddata with the digital filter in order to remove the extensive currentvariance is shown by the thick dotted line in FIG. 31(1), for the filtercharacteristic, a low-pass filter with a cut-off frequency of 100 [Hz]is used. It is to be noted that since the phase delay of about 72 [msec]from the measured data has occurred due to filtering, the result of thesignal processing in FIG. 31(1) is made coincident with the measureddata by forwarding the rising point by the phase difference. Thus, thediagonally rising component having moved before 0 [sec] shown in FIG.31(1) is neglected.

[0318] The identification processing is performed by using the data towhich the filter processing is performed. However, it is expected thatan error greatly influences the identification result since the currentvariance occurring at the start of the motor rotation is not completelyexcluded. Since this error is an uncertain variance dependent on apositional relationship between the brush and the commutator at thestarting time described before, the identification is performed fromthis view point.

[0319] 3.2.2 Time History Sensitivity of Characteristic (Process 38)

[0320] It has been clarified that all of the characteristic values whichform the subject of the identification are included in the motor currentI_(M) obtained from the output state value of the output equation in the5th row of Eq.(36). Therefore, in order to perform the identification,it is necessary to form a hypothesis by fully grasping the relationshipbetween the characteristic values which form the subject and the motorcurrent, and to verify it through the interpretation of theidentification result. Accordingly, the followings are hypothesized forthe influence of the characteristics on the motor.

[0321] {circle over (1)} It is supposed that the moment of inertia J_(M)is proportional to the change of the angular velocity (angleacceleration), provides strong influences during the acceleration whenthe change of the angular velocity is large, and does not provideinfluences in the steady state when the change is little.

[0322] {circle over (2)} It is supposed that the inductance L_(M) isproportional to the change of the motor current, provides influenceswhen the current variance is large at the starting moment, and does notprovide influences in the steady state when the current becomes stable.

[0323] {circle over (3)} It is supposed that the viscous resistancecoefficient δ_(M) is proportional to the angular velocity, providesinfluences as the load of the internal loss with the increase of theangular velocity, and assumes a fixed load in the steady state.

[0324] {circle over (4)} It is supposed that the kinetic friction torqueF_(MF) is a constant and so is a characteristic not influenced by thestate value change. The motor has a structure of reducing the frictiontorque as much as possible, so that the influence is little.

[0325] It is seen from the above-mentioned contents that the respectiveways in which the characteristic values contribute are different in theprocess of a time transition from the starting moment to the steadystate. Accordingly, if the sensitivity of the characteristic values forthe change of the motor current I_(M) is supposed to be the index of theidentification, it can be easily presumed that the identification ispossible. It is to be noted that the above-mentioned {circle over (3)}and {circle over (4)} are added for examining the time historysensitivity of the steady internal characteristic value.

[0326] The sensitivity of each characteristic value will be examinedbased on the above-mentioned result as follows (see flow chart in FIG.32):

[0327] It has been described that the simulation result in FIG. 31(1)has been performed at the initial values of the characteristic values,and the current deviation ΔI_(ERR) at that time is shown in FIG. 31(2).In order to make this the index of the identification, the influencewhich the characteristic value variance provides to the currentdeviation ΔI_(ERR) is clarified, so that the relationship between thecharacteristic value and the motor current can be grasped. This currentdeviation ΔI_(ERR) can be expressed by the following equation (at stepS3), with a current measured value I_(M) _(—MEG) (at step S1 in FIG. 33)and a simulation value I_(M) _(—) _(SIM) (at step S2) being made adeviation:

ΔI _(ERR) =I _(M) _(—SIM) −I _(M) _(—MRG)   Eq.(51)

[0328] If the deviation ΔI_(ERR) lies within an allowable range, thetransient identification is completed after verifying the identificationresult by the simulation described later (at steps S4 and S10).

[0329] From equation (51), the value of the simulation is high withrespect to the measured value when the current deviation ΔI_(ERR) ofFIG. 31(2) is positive, and otherwise low. Moreover, an extensiveviolent variance is included at the starting moment. Accordingly, inorder to use the current deviation ΔI_(ERR) as a sensitivity of themotor current I_(M) for the characteristic change, it is required toremove the variance. Also, the offset quantity by the current deviationΔI_(ERR) is included in the sensitivity.

[0330] A variance current deviation ΔI_(P) _(—) _(ERR) of thecharacteristic representing the sensitivity is expressed by thefollowing equation to remove the variance portion of the deviation:$\begin{matrix}\left. \begin{matrix}{{\Delta \quad I_{P\quad {\_ e}\quad r\quad r}} = {I_{P\quad \_ \quad S\quad I\quad M} - I_{M\quad \_ \quad M\quad E\quad G} - {\Delta \quad I_{E\quad R\quad R}}}} \\{= {I_{P\quad \_ \quad S\quad I\quad M} - I_{M\quad \_ \quad S\quad I\quad M}}}\end{matrix} \right\} & {{Eq}.\quad (52)}\end{matrix}$

[0331] On the upper term of equation (52), the I_(P) _(—) _(SIM) (atstep S5) indicates the simulation value at the time when the transientinternal characteristic value is changed, and the initial value currentdeviation ΔI_(ERR) indicates the current deviation of the measured valueI_(M) _(—) _(MEG) for the simulation value I_(M) _(—) _(SIM) of theinitial value by Eq. (51).

[0332] From this equation, the variance current deviation ΔI_(P) _(—)_(ERR) of the characteristic value can use the deviation of thesimulation value I_(P) _(—) _(SIM) of each characteristic value variancefor the simulation value I_(M) _(—) _(SEM) of the initial value as asensitivity as shown in the lower term of Eq.(52). However, since anidentification accuracy is exceedingly evaluated by the initial valuecurrent deviation ΔI_(ERR), it is necessary to proceed theidentification while the ΔI_(ERR) is contained within an appropriaterange at any time during the identification.

[0333] 3.2.3 Identification Method by Time History Sensitivity Change

[0334] Firstly, the result (at step S6) upon having determined thecurrent deviation ΔI_(P) _(—) _(ERR) at the time when the characteristicvalues are changed by +10 [%] and −10 [%] with the time history is shownin FIGS. 33B-33E. The four diagrams show the sensitivity seen with thetime history at the time when the characteristic values are changed.FIG. 33(1) is a diagram for comparing the sensitivities at the timeindicated by thin dotted times in the lower four diagrams, so that thetime history data are stored.

[0335] Also, in the respective current deviations of FIG. 33(2)-33(5), Jindicates the moment of inertia, L the inductance, D the viscousresistance coefficient, and F the kinetic friction torque, which aregenerally referred to “P (parameter)” as mentioned above. However, sincethe vertical axis scale in each diagram is the same as that in eachdiagram on the lower term, it is impossible to compare the four diagramsin parallel. Also, the solid line shows a result upon having increasedthe characteristic of the Table 4 by +10 [%], and the dotted line showsa result upon having decreased the same by −10 [%]. This calculation isperformed to all of the characteristic values of the identificationsubject (at step S7).

[0336] It is seen that the characteristic becomes linear, since theresult upon having changed the characteristic value by ±10 [%] issymmetrical with respect to positive and negative in FIG. 33(1). Themoment of inertia J_(M) and the viscous resistance coefficient δ_(M)become directly proportional to the motor current, and the inductanceL_(M) becomes proportional to the motor current in which the positiveand negative are inverted. These relationships almost agree to theabove-mentioned hypothesis of the relationship between the motor currentI_(M) and the characteristic, so that the variance of the deviation isremoved.

[0337] The identification is performed with the current deviation ΔI_(P)_(—) _(ERR) at the time when the characteristic of FIGS. 33(1)-33(5) ischanged being made a time history sensitivity. In the identification,trials are repeated so that the current deviation lies within anallowable range by the following process (at step S4):

[0338] Firstly, since the positive current deviation is large in therange of 0.02-0.06 [sec] from the initial value current deviationΔI_(ERR) of FIG. 31(2), the moment of inertia J_(M) in FIG. 33(2) whosesensitivity is high in this region as shown in FIG. 33(1) is selected(at step S8) to be reduced (at step S9) in the identification of thecharacteristic values. Since a variance value DI_(ERR) at the startingmoment comes near the upper side as shown in FIG. 31(2), the inductanceL_(M) of FIG. 33(4) is increased.

[0339] As for the order of identifying these characteristic values, theabove-mentioned steps S2-S9 are sequentially executed from the valuehaving larger coefficient with the mutual relationship between theinitial value current deviation ΔI_(ERR) and each variance currentdeviation ΔI_(P) _(—) _(ERR) being made an index. Alternatively, when itis thought that the variances do not interfere with each other due tothe time difference as shown in FIGS. 33(2) and 33(4), theidentifications may be simultaneously performed by the steps S2-S9.

[0340] It is to be noted that since the characteristic curve of themoment of inertia J_(M) is similar to the current deviation ΔI_(ERR)shown in FIG. 31(2) in the curve of the time history change, only themoment of inertia J_(M) may be changed for the identification under thejudgment that the current deviation ΔI_(ERR) is most influenced by themoment of inertia J_(M).

[0341] Among above-mentioned processes, the process firstly performed inwhich the updated value of the moment of inertia J_(M) is determined isshown in FIGS. 34(1) and 34(2).

[0342]FIG. 34(1) is a diagram showing the current deviation ΔI_(ERR) inFIG. 31(2) up to 0.06 [sec]. Point A in FIG. 34(1) indicates a deviationquantity a which is desired to be reduced and the position (point of0.03 [sec]). FIG. 34(2) shows a sensitivity of the moment of inertiaJ_(M) at the point of 0.03 [sec] shown in FIG. 33(1), in which thesensitivity point 0 (abscissa) is adjusted to the point A in FIG. 34(1).

[0343] It is to be noted that the current deviation shown by the bothordinates is adjusted to the same scale. As for the deviation quantityα=0.2 [A] which is desired to be removed from FIG. 34(1), it is seenfrom FIG. 34(2) that the sensitivity becomes negative.

[0344] Accordingly, a correction ratio β=16.8 [%] of moment of inertiaJ_(M) is determined from an intersection B between the abscissa of thedeviation 0 in FIG. 34(1) and the extension of the negative sensitivityin FIG. 34(2). From the Table 4, the updated moment of inertia J_(M)assumes 0.4×10⁻⁶×(1−0.168)=0.3328×10⁻⁶.

[0345] The result upon having updated the moment of inertia J_(M) is athin dotted line showing the variance current deviation ΔI_(P) _(—)_(ERR) of FIG. 31(2). The above-mentioned process is expressed by thefollowing equations (at step S9): $\begin{matrix}\left. \begin{matrix}{\quad {\beta = \frac{\alpha (t)}{\lambda (t)}}} \\{\quad {P_{M\quad \_ \quad N\quad E\quad W} = {P_{M\quad \_ \quad O\quad L\quad D}\left( {1 - \frac{\beta}{100}} \right)}}}\end{matrix} \right\} & {{Eq}.\quad (53)}\end{matrix}$

[0346] In equations (53), the upper term is an equation of thecorrection ratio β of the characteristic value, and the lower term is anequation of the characteristic value after the update. As for thevariables in Eqs.(53), α (t) [A] is a current deviation quantity of theinitial value current deviation ΔI_(ERR) at a time “t”, λ(t) [A/%] is asensitivity of a characteristic value at a time “t” similarly, P_(M)_(—) _(NEW) is a characteristic value after the update, and P_(M) _(—)_(OLD) is a characteristic value before the update.

[0347] When the motor current and the characteristic value have anon-linear characteristic (time history sensitivity becomesasymmetrical) which are not proportional to each other, and the changerange of the characteristic value greatly exceeds ±10 [%], it isnecessary to again determine the sensitivity from Eq.(52) to repeat theidentification.

[0348] 3.3 Result of Transient Identification and Simulation (at StepS10)

[0349] (1) Identification result and simulation thereof

[0350] The characteristic values via the above-mentioned process of thetransient identification are shown in Table 5. As test data foridentification, a result of an idle test is used in which the settingtorque of the fixed torque transient test is assumed to be 0. TABLE 5CHARACTERISTIC NAME SYMBOL UNIT CHARACTERISTIC VALUE INERTIA MOMENTJ_(M) [Nm/(rad/sec²)] 0.335 × 10⁻⁶ INDUCTANCE L_(M) [H] 4.2 × 10⁻³

[0351] The simulation result in which the transient identificationresult is reflected is shown in FIG. 35. The simulation has beenperformed by providing the transient identification result of the Table5 and the characteristic value of the steady identification result ofthe Table 3 to the government Eq.(35) for execution of the fixed torquetransient test model shown in FIG. 23.

[0352] In FIG. 35(1), the result out of consideration of the inductanceL_(M) is added by a thin dotted line and the natural data of the motorcurrent are omitted, while the others are the same as FIG. 31. It is tobe noted that the simulation out of consideration of the inductanceL_(M) has been performed by the same Eq.(35) with L_(M)=10⁻¹² [H] beingmade a minute value without influence.

[0353] When both are compared, it is seen that by the influence of theinductance L_(M) the starting current which steeply rises at thestarting moment is saved as an energy to be released with the decreaseof the current.

[0354] As a result, the rising of the motor current and the peak valueare suppressed in a solid line (L_(M)>0) with respect to the thin dottedline (L_(M)≈0), and the falling part shifts to the right side. Also, theidentification results are almost coincident as shown by the currentdeviation ΔI_(ERR) of FIG. 35(2), while the variance at the startingmoment appears as it is in the form of the current deviation asmentioned before.

[0355] (2) Simulation of actual load transient test model of motor

[0356] Since this identified motor model becomes a virtual prototypewhich accurately reproduces a motor unit, a model of a virtual testwhich reproduces the driving state of the actual machine can be made byincorporating the functional model for operation in FIG. 26 into theactual load transient test model shown in FIG. 21 (see FIG. 42 describedlater). The simulation result will be described:

[0357] The simulation is performed by providing the characteristic valueof the electrically driven arm of the following Table 6 to Eq.(41).TABLE 6 CHARACTERISTIC NAME SYMBOL UNIT CHARACTERISTIC VALUEDECELERATION RATIO N_(G) 1/200 OPERATING ANGLE θ_(S) [rad] 1.57 STOPPERSTIFFNESS K_(S) [N/m] 7.0 × 10⁴ STOPPER RADIUS L_(S) [m] 0.04

[0358] It is to be noted that the characteristic values other than thosein the Table 6 are the same as those of the motor model after theidentification. Also, the motor operation is performed by the powersupply switch S_(WS), the regular/reversed rotation switch S_(WL), andthe braking switch S_(WB) shown in FIG. 26.

[0359] Firstly, the simulation result of the motor start and stop isgiven as follows:

[0360] The power supply switch S_(WS) is turned ON to start the motorwith the regular/reversed rotation switch S_(WL) being made OFF and thebraking switch S_(WB) being made OFF. Then, the load of 2 [N·m] isprovided after 120 [msec], and the power supply switch S_(WS) is turnedOFF after 280 [msec] with the load being provided. The simulation resultis shown in FIGS. 36(1)-36(4).

[0361] FIGS. 36(1)-36(4) sequentially show a motor current I_(M) [A], aload torque T_(L) [Nm], a motor voltage V_(M) [V], a motor angularvelocity ω_(M) [rad/sec], and an operation angle of arm θ_(R) [deg]. Themotor current I_(M) increases, and the angular velocity ω_(M) decreasesfrom the moment the load is added. At this time, the change of theoperation angle θ_(R) becomes rather slow.

[0362] Also, when the power supply is OFF, the motor current I_(M)aassumes 0 [A], the angular velocity ω_(M) decreases, and the loaddrives the motor from about 310 [msec], so that the reversed rotationoccurs. Also, as for the motor voltage V_(M) after the power supply isturned OFF, the inductance L_(M) generates a spike-like voltage at themoment the power supply is OFF, and then the induced voltage V_(W) bythe angular velocity ω_(M) of the motor occurs, so that the state wherethe motor is changed into the generator is described.

[0363] The simulation result of the motor regular/reversed rotation, aswell as the braking is as follows:

[0364] FIGS. 37(1)-37(4) show an example in which the motor is stoppedwith and without the electric brake by OFF/ON of the braking switchS_(WB). FIGS. 37(1)-37(4) show states as follows: The motor is startedwith the regular/reversed rotation switch S_(WL) being made OFF and thepower supply switch S_(WS) being made ON to be compulsorily stopped withthe stopper. Then, the motor is rotated reversely in 0.6 [sec] after theregular/reversed switch S_(WL) being made ON and is again compulsorilystopped with the stopper on the opposite side. After the lapse of 1.4[sec], the power supply switch S_(WS) is made OFF, so that the motor isstopped.

[0365] FIGS. 37(1)-37(4) sequentially show a motor current I_(M) [A], anoperation angle of arm θ_(R) [deg], a motor angular velocity ω_(M)[rad/sec], and a motor torque T_(M) [Nm]. Also, the solid line of FIGS.37(1)-37(4) shows the state where the electric brake by the brakingresistance of R_(L)=1 [mΩ] is applied, while the dotted line shows thestate without the brake. Both of the regular and reversed rotationsgenerate an attenuated oscillation at the time of collision at thestopper.

[0366] Also, when the motor is compulsorily stopped by the stopper, aflexible structure of a stopper gum absorbs the maximum torque of themotor to be compressed. If the power supply is cut off in this state,the arm is pushed back by the compression power owing to the bend of thestopper. The effect of the electric brake appears at the stop stateafter 1.4 [sec]. It is seen from the motor angular velocity ω_(M) andthe operation angle of arm θ_(R) that the motor idle by the stoppercounterforce is well absorbed as shown by the solid line.

[0367] 4. Motor Identification Apparatus

[0368] Hereinafter, an apparatus required for identifying theabove-mentioned test model of the steady and transient states of thefunctional parts will be described.

[0369] The functional parts incorporated into the products have astructure working with assembling the functional parts to theinput/output portion and by mutually associating them. As shown in FIG.21, the battery for driving, the electrically driven arm forming a loadand the like are necessary e.g. for the motor test. Also, in order tocontrol the motor, the operation system shown in FIGS. 25, 27, and 29 isnecessary.

[0370] The test model for thus performing an identification by using thetest data of the actual machine requires modeling the functional partswhich form the driving source and the load of the tested object. Subjectto the influence by other functional parts in this way, it is possibleto standardize the function and the behavior as the specification of thetested object and the test standard. If there is something whichoperates and reproduces the behavior just as standardized function andbehavior, it can be used as a substitute common to each test method.

[0371] If the functional model which forms the substitute of the drivingsource and the load is modeled and incorporated into the test model asan identified model, the identification defined to the internalcharacteristic value of the tested object can be performed. Also, as forthe testing unit and the test model, the group of the tested objects atwhose parts specifications are different can be replaced with blockreplacement means.

[0372]FIG. 38 shows an example in which the concepts are applied to themotor identification apparatus. In the identification apparatus of FIG.38, a motor model 1 has a block replacement structure for a testreproduction model 22. A reproduction model 2 is composed of a drivemodel 21 such as a battery and an operation model, a load model 22 suchas a load generator model, an observation model 23 for observing thestate value of the motor model 1, and a characteristic update model 24for updating the internal characteristic value.

[0373] Also, a drive circuit unit 4 takes charge of the battery and theoperation system which forms driving source of a tested motor 3. A loadgenerator 5 and an electric load 6 take charge of the mechanical load ofthe motor 3. The driving source and the mechanical load are modeled as adrive model 21 and a load model 22 already identified.

[0374] Furthermore, as for the relationship between the actual machinetesting unit and the models, the former drive model 21 has a structureof controlling the drive circuit unit 4 through a drive system controlunit 71, and the latter load model 22 has a structure of controlling theload generator 5 through a load system control unit 72.

[0375] The measured value of the motor 3 is obtained at a measurementunit 8 to be provided to a calculation unit 9. The calculation unit 9receives the observation value of the observation model 23 and themeasurement from the measurement unit 8 to update a characteristicupdate model 24.

[0376] The motor drive model 21 shown in FIG. 38 corresponds to themodel in which the battery on the left side of FIG. 21 and the operationsystem circuit of FIGS. 26, 28, and 30 are united. The load generatormodel 22 corresponds to the arm mechanism on the right side of FIG. 21and the fixed torque generation model of FIG. 23.

[0377] The operation and the behavior reproduced on the former model arereproduced at the drive circuit unit 4 by the drive system control unit71 to be provided to the motor 3 of the tested object. Similarly, theload state provided to the motor 3 is reproduced on the load generator 5by the load system control unit 72 controlling the electric load 6.

[0378] Accordingly, if the motor functional model 1 shown in FIG. 4(1)is connected to the reproduction model 2, the drive model 21 and theload model 22 preliminarily provided corresponding to the drive systemand the load system turns into predetermined steady test models andcontrol the drive circuit unit 4 and the electric load 6.

[0379] Together with this operation, the steady test data generated bythe motor 3 is measured by the measurement unit 8, so that thecalculation unit 9 which has received the data provides the governmentequation of the steady functional model i.e. the steady internalcharacteristic value to the motor model 1. Thus, the steadyidentification is completed.

[0380] Then, the reproduction model 2 changes the drive model 21 and theload model 22 into the transient test model for the transientidentification. Together with this operation, the transient test isexecuted, so that the transient test data are provided to thecalculation unit 9 from the measurement unit 8. The calculation unit 9compares the value obtained by substituting the already calculatedsteady internal characteristic value for the internal characteristicvalue inputted from the observation model 23 with the measured data fromthe measurement unit 8. The internal characteristic value of the motormodel 1 is properly updated as mentioned above, by the characteristicupdate model 24 so that an error between them becomes small to liewithin an allowable range. Thus, the transient identification ends, sothat all of the identification works are completed.

[0381] 5. Virtual Test

[0382] The model which reproduces the function, the performance, and thecharacteristic value of the parts or the products composed of the partsis identified with the test data of the actuality, so that the internalcharacteristic value and the reproductivity are verified. Therefore, itcan be utilized as a virtual prototype for performing various evaluationtests on the computer instead of the actual machine. An example wherethe virtual prototype is applied to the product development will bedescribed.

[0383] 5.1 Relationship Between Product Development and Modeling

[0384] The development process of product and the formation process ofmodel are shown by the relationship in FIG. 39.

[0385]FIG. 39 shows a relationship between a development process of aproject stage, a design stage, a prototype and test stage, and modelingand identification performed in parallel with the development. FIG. 39shows a process including a concept of concurrent engineering in whichdevelopment processes of the product are performed in parallel.

[0386] On the other hand, the process of modeling has a flow as follows:An abstract product target at the project stage is firstly modeled, themodel is sequentially detailed along with the development process, avalidity of the project and design contents by the model is verified,and the identification is finally performed with the test data of theprototype, so that the virtual test is performed executing computersimulation with the identification model instead of the variousevaluation tests.

[0387] (1) Relationship between product development and modeling

[0388] If this virtual test is applied to the development of the car,for example, the process is given as follows:

[0389] As for the abstract model at the project stage such as a modelreproducing a car performance which forms the target characteristics ofthe projected car such as an acceleration time of 0-400 [m], astationary fuel cost performance, and an acceleration performance, apossibility of achieving the target characteristics is verified by themodel assembled with the characteristics such as a weight, a resistance,a power characteristic, and a gear ratio.

[0390] In the next product part formation and basic design, the model isdisassembled following the formation method of the basic functionalparts such as an engine, a transmission, a body realizing the targetcharacteristics, and modeling is performed with the internalcharacteristic values of the functional parts being clarified, so thatthe function and the performance required for the functional parts, andthe possibility of realizing the target characteristic value areverified.

[0391] Similarly, in the detailed design, the modeling is detailedaccording to the contents of the detailed design of an engine, a clutch,a brake, a transmission, a control unit, and the like, whereby thevalidity of the design contents in the details such as a torquevariance, a transmission shock, a vibration noise, and the controlalgorithm is verified with the model.

[0392] The model made by the above-mentioned process is sequentiallydisassembled into the lower model from the abstract model at the projectstage along the part formation, so that a systematic model hierarchizedfrom the product to the part and its details is formed.

[0393] Also, in the above-mentioned process of the project, design andmodeling, the development is proceeded by the design drawing of theactuality realizing the target characteristic, and the model verifyingthe design drawing. However, both are eventually hypotheses.Accordingly, it is necessary to test both by an experimental car madebased on the design drawing or the like, to identify the model, and toverify the validity of the hypotheses.

[0394] (2) Relationship between product development and identification

[0395] By the above-mentioned reason, the identification shown in thelower side of FIG. 39 is performed based on the data of the functionalparts and a car test. The identification and the test necessary thereforare performed separately in the above-mentioned steady and transientstates. The model is converted in accordance with the test method asshown in FIGS. 9-13 for the steady test, and in FIGS. 21-30 for thetransient test, so that the identification of the test model isperformed by the test data. In the identification performed here, theidentification result of the test models firstly performed perfunctional part are integrated along the part formation to be made theidentification of the product model.

[0396] It is supposed that the functional parts of the battery, themotor, and the electrically driven arm in FIG. 21 are individuallymodeled, for example, and the product in which the parts are mutuallyconnected is an electric actuator. The model of the electric actuatorcan be represented by a hierarchized model where the models of thebattery, the motor, and the electrically driven arm are made lowerlevel. Also, it is seen that the equation in which the models areintegrated turns into Eq.(34).

[0397] Accordingly, if the motor at the center of FIG. 21 and theelectrically driven arm on the right side of FIG. 21 are individuallyidentified, and they are made the characteristic value of FIG. 21, theelectric actuator on the upper hierarchy is naturally identified. Themodel which can faithfully reproduce the characteristic value and theperformance of the product along the mode of the actuality is called a“virtual prototype”.

[0398] As an applying method of this hierarchized model, it is possibleto convert the electric actuator model into the test model when thebattery and the electrically driven arm are already identified in FIG.21, to make the identified internal characteristic values fixed values,and to perform a partial identification defined to an unfixed internalcharacteristic value of the motor model.

[0399] 5.2 Virtual Test by Virtual Prototype

[0400] Thus identified part model is confirmed as a virtual prototype ofthe part reproducing the function and the performance held by each part.Similarly, the product model in which the part models already identifiedare mutually connected and integrated means that the model is confirmedas a virtual prototype reproducing the product. Also, these modelsresult in the identification by converting the models into test modelsin accordance with the above-mentioned test methods, comparing andevaluating the reproducibility and the test result of the actuality, andverifying the difference between both.

[0401] Accordingly, the virtual prototype of the identified product andparts can be evaluated as to the validity by performing the computersimulation instead of the various tests of experimental articles and thelike. Performing the evaluation test of the actuality on the computer byutilizing the virtual prototype is called a “virtual test” of the partsor the product. The simulation which has verified the validity of theidentification result obtained in FIGS. 37-38 is also one example.

[0402] (1) Internal formation of virtual test model

[0403] The virtual test is usually applied with the test standarddetermining the same environment condition as the evaluation test of theactuality, a test condition and performance such as driving operation,and an evaluation standard such as a success/failure judgment, aperformance prediction, a presence/absence of abnormal phenomena, andthe like by the reproduction data of the performance and the behavior.This can be typically shown in FIG. 40.

[0404]FIG. 40 is a basic arrangement of a virtual test model 10 where avirtual prototype 11 and a test standard group 12 applied thereto aremodeled to be incorporated. The virtual prototype in this arrangementcomprises two kinds of functional part models, an already identifiedfunctional part model and a functional part model in which design valuesand characteristic values of similar parts are provided upon the virtualtest.

[0405] Both are distinctive in that the former is used as a model of apart having a basic function governing the function and the performanceof the product, while the latter is used as a model extended for variousproducts whose characteristic values are different in the application,the structure, and the shape of the products. The product model havingboth models united indicates that the evaluation test of variousproducts and the test evaluation of various product groups where thederived functional models (latter) of different characteristic valuesare combined with the identified basic functional model (former) can beperformed by the virtual test.

[0406] (2) Virtual prototype example of virtual testing car

[0407] For example, a simple model of the virtual prototype for car inFIG. 40 can be shown in FIG. 41. FIG. 41 is an example of a virtualprototype showing a functional model where the functional parts of theengine and the transmission are modeled to be integrated as a powertrain (P/T), and a functional model where various types of cars whichare different in a structure and the like are modeled as a body, andboth are integrated as a car. Between the models of the functionalparts, the engine and the transmission are related with the angularvelocity ω_(e) that is a potential quantity, and with the torque T_(e)that is a flow quantity, while the transmission and the body are relatedwith the torque v_(b) that is a potential quantity, and with the torquef_(b) that is a flow quantity.

[0408] The characteristics in the models are as follows:

[0409] The characteristics of the engine are a moment of inertia J_(e),a viscous resistance coefficient δ_(e), and an internal torque T_(e).δ_(e) and T_(e) among them respectively indicate a partially linearizedgradient, and a generation torque when the engine rotation number is“0”, in the rotation—torque characteristic in which a throttledivergence α_(e), an atmospheric pressure p_(e), and an air temperaturet_(e) are inputted.

[0410] The characteristics of the transmission are a gear ratio N_(m), afinal gear ratio N_(d), and a stiffness of an output axis C_(p).Finally, the characteristics of the body are a tire radius R_(T). a massof the body M_(b), a resistance during running D_(b), and an ascent of arunning road θ. Since the characteristics of the body, R_(T), M_(b), andD_(b) are different according to the body structure or the like, theyare required to be replaced according to the type of car.

[0411] Moreover, the control characteristic values of the engine controlunit and the transmission control unit respectively controlling theengine and the transmission are preliminarily identified by the testdata.

[0412] In order to perform a virtual test under the same condition asthe car driving state by using the virtual prototype in FIG. 41, thefollowing model is further required. Firstly, for the gradient θ, theatmospheric pressure p_(e), the air temperature t_(e) and the like, theenvironment condition model provided as an environment condition forusing cars is required. Furthermore, for the throttle divergence α_(e),and a gear ratio N_(g) of the transmission, the operation model whichprovides the driving operation condition of cars is required.

[0413] Finally, in order to evaluate the reproduction result of thevirtual prototype in which the virtual test has been performed, theobservation model for observing ω_(e), v_(b), T_(e), f_(b), and the likeis required. The model for the conditions and for performing theevaluation is modeled based on the evaluation test methods of the teststandard models shown in FIG. 40.

[0414] The virtual test performed by the model can be applied to varioustypes of cars by incorporating the characteristic values of the bodymodel which are different depending on the type of car into the virtualprototype of the functional part in the P/T which faithfully reproducesthe characteristic value, performance, and behavior of the actualmachine. The virtual test using the virtual prototype of the car modelcan be substituted for the various tests performed to various types ofcar on which the same P/T is mounted.

[0415] In the development of cars so far, the evaluation tests extendingover a wide range have been performed to the cars having many bodystructures, so that the validity as a commodity has been verified. Insuch vast numbers of tests, the number of cars made on the experimentalbasis can be reduced and the development period required for the testcan be shortened by the virtual test performed with the bodycharacteristic value in the virtual prototype being replaced and withmodeled test standard groups being combined. As a result, cost for adevelopment can be reduced.

[0416] 5.3 Virtual Testing System (Model)

[0417] The basic arrangement of the actual machine test and theidentification as well as the virtual test by the virtual prototype canbe shown as in FIG. 42.

[0418] It has been already described for the virtual test of the virtualprototype that the tested model which forms a subject of the actualmachine test can be converted into the test model, the test standard canbe modeled as the driving operation model and the environment conditionmodel, and the execution result of the virtual prototype can be modeledas the observation model.

[0419] It is possible to compare and evaluate both of the observationdata reproduced by the virtual test and the test data of the actualmachine test and to perform the identification. This indicates that thevirtual test model and the test model for the identification can be madecommon.

[0420] Namely, the virtual testing system (model) can have consistencyof the actual machine test, the identification, and the virtual test byincorporating an identification model 9 (corresponding to a calculationunit in FIG. 38) which models the identification technique and processto update the internal characteristic value of the virtual prototype,and an evaluation model 35 in which the virtual test verifies thevalidity of the reproduction data.

[0421] In FIG. 42, the actual machine test data are inputted to theevaluation model and to the identification model, and the result of theevaluation model is inputted to be related. As for both models, thefollowing processes are performed: In case a problem arises in theresult of the success/failure judgment in the virtual test, the formerperforms the actual machine test to evaluate again as shown in a dottedline, and in case there is a problem in the identification result of thevirtual prototype, the latter can perform the identification again.

[0422] Main contents of each portion in the virtual testing system shownin FIG. 42 are given as follows:

[0423] {circle over (1)} The virtual prototype 1 corresponds to themotor model in FIG. 38, and a model which reproduces the function, theperformance, and the characteristic value of the product and part, whichis composed of a mechanical system model relating to the mechanism andthe structure, and the control system model for controlling them.

[0424] {circle over (2)} A driving operation model 31 is a model foroperating the virtual prototype 1 by the same driving condition as theactual machine. For example, there are an accelerator operation, atransmission operation, a braking operation of a car, and the like.

[0425] {circle over (3)} An environment condition model 32 is a modelfor reproducing the influence of the environment with the virtualprototype 1 being made operate under the same condition as theenvironment for using the actual machine. For example, there are arunning road, a temperature, a humidity, an atmospheric pressure, andthe like when the car makes a market running.

[0426] {circle over (4)} An observation model 23 is a model forreproducing the data, on the model, at each measurement point measuredin the actual machine test as shown in FIG. 38, so that the data whichare difficult to be measured in an actual machine can be observed on themodel.

[0427] {circle over (5)} An internal characteristic update model 24 is amodel for updating the internal characteristic value of the virtualprototype based on the identification result, as shown in FIG. 38.

[0428] {circle over (6)} An actual machine test portion 33 performs atest which has been performed in the prior art, and a measurement unit34 in the actual machine test portion 33 shows a unit for taking thetest data into a computer on real time basis.

[0429] The above-mentioned operation can be summed up as follows: If thedriving operation condition and the environment condition are providedto the virtual prototype 1 from the models 31 and 32 respectively, thevirtual prototype 1 provides the reproduction data obtained as a resultof the simulation at this time to the evaluation model 35 from theobservation model 23. The evaluation model 35 evaluates, instead of theactual machine test, whether or not the reproduction data of thesimulation result is valid (validity of product/part) by comparing theevaluation reference determined by the test standard with thereproduction data.

[0430] In case of a failed evaluation, the evaluation model 35 instructsthe virtual test portion 30 to change the driving operation conditionand the environment condition, and to conduct the identification againif the deviation between the actual machine test data and thereproduction data is large. Thus, as shown in the dotted line in FIG.42, the driving operation condition and the environment condition arerespectively provided to the actual machine test portion 33 from themodels 31 and 32.

[0431] Accordingly, if the deviation between the actual machine testdata from the actual machine test portion 33 through the measurementunit 34 and the reproduction data is large, the evaluation model 35instructs the identification model 9 to conduct the identificationagain. Since the identification model 9 corrects the internalcharacteristic value of the virtual prototype 1 by the internalcharacteristic update model 34 after receiving the actual machine testdata, the evaluation is performed again at the evaluation model 35through the observation model 23. As a result, the identification model9 updates the internal characteristic value of the virtual prototype 1so that the deviation between the actual machine test data and thereproduction data becomes least.

[0432] When the evaluation ends, another modification (e.g. modificationaccording to a type of car) in the virtual prototype 1 is evaluated.Particularly, by performing the identification to the internalcharacteristic value (control parameter) of the control model in thevirtual prototype, the virtual prototype 1 can be tuned, according tothe type of car, to an optimum state.

[0433] 5.4 Actual Machine Test of Transmission Applying Virtual Test

[0434] In case the actual machine test is performed to a unit of thefunctional part of the tested object, the functional part of the drivesystem for driving the part and the functional part of the load systemwhich forms the load are required. The method in which the virtualprototype where such a drive system functional part and a load systemfunctional part are identified is incorporated into the testing unit,and the actual machine test of the tested object is performed will nowbe examined.

[0435]FIG. 43 shows an example of a testing unit of a transmissionincluded in the virtual prototype.

[0436]FIG. 43 shows a unit for reproducing the drive characteristic ofthe engine and the load characteristic of the car by the drive motor andthe load generator, and for performing the actual machine test to thetransmission proper along the actual use state. This unit derives theperformance required for the running of the car from the engineperformance which forms the drive source, and the evaluate test aboutvarious performances relating to the acceleration such as anacceleration performance or a gearshift shock is performed by the actualmachine.

[0437] In FIG. 43, the testing unit 33 is composed of the drive motor 4for driving the transmission, the load generator 5 which forms the load,the electric load 6, and the measurement unit 8 for measuring thedriving state. Also, to the testing unit 33 the virtual prototype 11 ofthe drive motor 4, the load generator 5 and the transmission 50 isconnected for reproducing the driving state of the car. The testing unit33 comprises the drive system control unit 71 and the load systemcontrol unit 72 which faithfully reproduce the result at the drive motor4 and the load generator 5.

[0438] These control units 71 and 72 provide the drive torque of theengine to the transmission 50 by the drive motor 4, and reproduce theload torque provided to the transmission 50 through the body by the loadgenerator 5 and the electric load 6. Both models of the drive motor 4and the load generator 5 can reproduce the function, the performance,and the behavior of the engine and the body in the testing unit 33 bythe identified virtual prototype 11.

[0439] The virtual prototype in which these drive motor 4, transmission50 and load generator 5 are modeled is shown in FIG. 44. It is to benoted that the operation system models 31, 38, 39, and the environmentsystem model 32 inputted to the transmission model 1 in FIG. 43 are themodels of the test standard mentioned above. The former represents themodel which inputs the driving operation condition of the driver, andthe latter inputs the environment condition such as an air temperaturewhich influences the internal characteristic value of the transmission.Also, the observation system models 23, 37, and 40 are models forobserving the state values inside the virtual prototypes.

[0440] The motor model 13 of the drive system in FIG. 44 is composed ofthe models of the motor, the battery, the fixed current controlrespectively in FIGS. 6, 4A, and 30. It is to be noted that a part ofthe internal characteristic values of the motor model 13 are omitted forsimplified description of the model. Similarly, on the load side, theload generator model 14 is composed of the braking resistance in FIG.4(1) and the electric load is composed of the braking resistance on theleft a side of FIG. 13, where a part of the internal characteristicvalues is omitted.

[0441] In FIG. 44, the angular velocity co, and the input torque Teconnecting the drive system motor model 13 and the transmission model 1are provided as target values of the drive system control unit 71.Similarly, a velocity vb and a drive force fb of the load generatormodel 14 are provided as target values of the load system control unit72. Also, the drive torque Te of the drive motor 4 can be reproduced bycontrolling the control current I_(CNT) with the operation system fromthe fixed current control model in FIG. 30 and by adjusting the currentof the drive motor.

[0442] Also, the load torque of the load generator 5 can be reproducedby being changed into the braking resistance value through the operationsystem from the motor model of the braking test in FIG. 13. Also, as forthe load torque, the fixed current control model and its unit in thesame FIG. 30 as the drive motor may be incorporated instead of thebraking resistance.

[0443] Moreover, into the testing unit 33 shown in FIG. 43, the internalcharacteristic update model 24 is incorporated in which the evaluationmodel (see FIG. 42) provided in the calculation unit 9 evaluate adifference if generated between the test result of the transmission 50and the reproduction result of the virtual prototype 11, and theidentification model (see FIG. 42) similarly provided in the calculationunit 9 identifies the internal characteristic value in the transmissionmodel (virtual prototype) 1.

[0444] 5.5 Actual Machine Test Example of Power Train (P/T) ApplyingVirtual Test

[0445] An example will be examined in which the actual machine test ofthe P/T where the engine and the transmission, except the motor 4 on thedrive side and the virtual prototype 13 and the drive system controlunit 71 leading to the motor 4, are united is performed in the actualtesting unit shown in FIG. 43.

[0446] The example of the actual machine testing unit having the P/T andthe load generator is shown in FIG. 45.

[0447] In this example, the same P/T 51 is mounted on the bodies whosestructures are different shown in FIG. 41, and the actual machine testis performed to the various cars, so that the adaptability of the P/T 51for the cars having the different body characteristic values isverified.

[0448] The virtual prototype 1 of the P/T 51 requires many kinds inwhich transmissions such as an automatic transmission, a manualtransmission, and a continuously variable gear are combined in additionto kinds of motors such as a motor, a gasoline engine, and a dieselengine. Accordingly, these virtual prototypes 1 are replaced with amodel replacement unit 25.

[0449] Also, the virtual prototype 1 of the P/T 51 has a control modelfor reproducing the software of the control unit of the engine and thetransmission, so that the virtual prototype 1 can control the model ofthe virtual prototype 1 and can directly control the actual machine ofthe engine and the transmission through a control input/output unit 36.

[0450] Accordingly, when adaptability of the body performance reproducedat the actual machine P/T 51 and the load generator 5 is worsened, theinternal characteristic value of the control model can be changedthrough an internal characteristic update unit 24. Then, the measureeffect is firstly confirmed in the virtual prototype 1, and secondly theadaptability is again evaluated by the actual machine test to beconfirmed.

[0451] In case a low-speed region torque of engine mounted on a carwhose body weight or running resistance is large is insufficient and thestarting acceleration power is insufficient, the control characteristicvalue of the control model is updated through the internalcharacteristic update unit 24 in order to improve the accelerationperformance and increase the fuel amount at starting the acceleration.The measure effect of the control amount change and the bad influence onthe fuel-efficiency and the exhausted gas worried because of theincreased amount are firstly confirmed by the virtual test in thesimulation of the virtual prototype, and the actual machine evaluationtest of the P/T 51 is finally performed by using the virtual testingunit.

[0452] By this process, the tests for reproducing bad influence whichexamines the measure effect and many test items can be verified by thesimulation of the virtual prototype in a short time, so that it becomespossible to define the actual test items requiring a long time based onthe verification result, and to perform an efficient test operation.

[0453] As described above, a characteristic value identification methodand an apparatus therefor according to the present invention is arrangedso that a functional model of a part is prepared based on a potentialquantity and a flow quantity representing a strength and a quantity ofenergy applied to the part, a steady internal characteristic value ofthe functional model in a steady state is identified, and a transientinternal characteristic value of the functional model in a transientstate is identified by using the identified steady internalcharacteristic value. Therefore, the function of all of the articleswhich govern two dimensions of the potential quantity and the flowquantity prescribing the energy can be modeled.

[0454] Also, since the transient identification is performed to theinternal characteristic value of the model after the steadyidentification, the steady identification without an interference of atransient state can be performed. Since the transient identification isperformed based on the steady identification, there is an effect ofobtaining an accurate internal characteristic value by a simplifiedidentification process. Also, it is possible to faithfully reproduceproducts/parts in the steady state and the transient state.

[0455] Moreover, by manufacturing an apparatus which uses such acharacteristic value identification method, it becomes possible topromptly perform the identification of the functional model of the samekind.

[0456] Furthermore, the characteristic value identification apparatusaccording to the present invention is arranged such that the functionalmodel having the characteristic value identified by the identificationapparatus is incorporated in a virtual testing system as a virtualprototype, reproduction data are obtained from the virtual prototype byproviding a driving operation condition and an environment condition,actual machine test data obtained by the driving operation condition andthe environment condition are compared with the reproduction data, and are-identification is performed depending on the comparison result as theoccasion demands. Therefore, it becomes possible to omit the actualmachine test performed in a development process of a design,prototyping, and a test, to shorten a period, and to reduce cost for adevelopment.

1. A characteristic value identification method comprising: a firstprocess for preparing a functional model of a part based on a potentialquantity and a flow quantity representing energy applied to the part, asecond process for converting the functional model into a steadyfunctional model in a steady state to identify a steady internalcharacteristic value, and a third process for identifying a transientinternal characteristic value of the functional model in a transientstate by using the steady internal characteristic value.
 2. Thecharacteristic value identification method as claimed in claim 1 whereinthe second process includes; a first step for determining an internalcharacteristic value of at least one steady test model from the steadyfunctional model, a second step for collecting steady test data byperforming a test corresponding to the steady test model, and a thirdstep for identifying a steady internal characteristic value of theinternal characteristic value based on the steady test data.
 3. Thecharacteristic value identification method as claimed in claim 2 whereinthe first step determines the internal characteristic value from agovernment equation in the steady state of the functional model.
 4. Thecharacteristic value identification method as claimed in claim 3 whereinthe third step converts the government equation into a recurrenceequation to determine the steady internal characteristic value from arecurrence coefficient of the recurrence equation.
 5. The characteristicvalue identification method as claimed in claim 2 wherein the third stepdivides the steady internal characteristic value into a known factor andan unknown factor to identify the steady internal characteristic valueof the unknown factor.
 6. The characteristic value identification methodas claimed in any one of claims 1 to 5 wherein the third processincludes; a first step for determining an internal characteristic valueof at least one transient test model in a transient state of thefunctional model, a second step for collecting transient test data byperforming a test corresponding to the transient test model, a thirdstep for applying the steady internal characteristic value to theinternal characteristic value of the transient test model to generatetransient phenomenon reproduction data, and a fourth step for correctingthe transient phenomenon reproduction data based on an error between thetransient phenomenon reproduction data and the transient test data,thereby identifying a transient internal characteristic value.
 7. Thecharacteristic value identification method as claimed in claim 6 whereinwhen the error does not lie within an allowable range the fourth steprepeatedly corrects a predetermined transient internal characteristicvalue within the transient phenomenon reproduction data until the errorlies within the allowable range, and determines the transient internalcharacteristic value to be identified when the error lies within theallowable range
 8. The characteristic value identification method asclaimed in claim 7 wherein the fourth step preliminarily calculates avariance deviation, as a time history sensitivity, to an initial valueat a time when each transient internal characteristic value is increasedor decreased at a fixed ratio, and selects a transient internalcharacteristic value having a maximum sensitivity within the timehistory sensitivity as the predetermined transient internalcharacteristic value.
 9. The characteristic value identification methodas claimed in claim 7 wherein the fourth step preliminarily calculates avariance deviation, as a time history sensitivity, to an initial valueat a time when each transient internal characteristic value is increasedor decreased at a fixed ratio, and selects a transient internalcharacteristic value having the time history sensitivity similar to theerror as the predetermined transient internal characteristic value. 10.The characteristic value identification method as claimed in claim 9wherein the fourth step simultaneously selects a plurality of transientinternal characteristic values having different maximum sensitivitytimes as the predetermined transient internal characteristic value. 11.A characteristic value identification apparatus comprising: blockreplacement means for a functional model of a part prepared by apotential quantity and a flow quantity representing a strength and aquantity of energy applied to the part, test reproduction means forreproducing at least one steady test model in a steady state of thefunctional model and at least one transient test model in a transientstate, testing means of the part for performing a steady test and atransient test respectively corresponding to the steady test model andthe transient test model, measurement means for collecting steady testdata and transient test data at a time when a steady test and atransient test of the part are performed by the testing means, andcalculating means for identifying a steady internal characteristic valueof the steady test model by using the steady test data, for applying thesteady internal characteristic value to the transient test model togenerate transient phenomenon reproduction data, and for correcting thetransient phenomenon reproduction data based on an error between thetransient phenomenon reproduction data and the transient test data,thereby identifying a transient internal characteristic value.
 12. Thecharacteristic value identification apparatus as claimed in claim 11wherein when the error does not lie within an allowable range thecalculating means repeatedly correct a predetermined transient internalcharacteristic value within the transient phenomenon reproduction datauntil the error lies within the allowable range, and determine thetransient internal characteristic value to be identified when the errorlies within the allowable range
 13. The characteristic valueidentification apparatus as claimed in claim 11 wherein the calculatingmeans preliminarily calculate a variance deviation, as a time historysensitivity, to an initial value at a time when each transient internalcharacteristic value is increased or decreased at a fixed ratio, andselect a transient internal characteristic value having a maximumsensitivity within the time history sensitivity as the predeterminedtransient internal characteristic value.
 14. The characteristic valueidentification apparatus as claimed in claim 11 wherein the calculatingmeans preliminarily calculate a variance deviation, as a time historysensitivity, to an initial value at a time when each transient internalcharacteristic value is increased or decreased at a fixed ratio, andselect a transient internal characteristic value having the time historysensitivity similar to the error as the predetermined transient internalcharacteristic value.
 15. The characteristic value identificationapparatus as claimed in claim 13 wherein the calculating meanssimultaneously select a plurality of transient internal characteristicvalues having a different maximum sensitivity time as the predeterminedtransient internal characteristic value.
 16. A virtual testing systemwhich incorporates a functional model, as a virtual prototype, having aninternal characteristic value identified by a characteristic valueidentification apparatus claimed in claim 11 comprising: conditionassigning means for assigning a driving operation condition and anenvironment condition to the characteristic value identificationapparatus, observation means for observing reproduction data obtained bythe virtual prototype when the driving operation condition and theenvironment condition are assigned, and evaluation means for evaluatingan observation result of the observation means.
 17. The virtual testingsystem as claimed in claim 16, further comprising another measurementmeans for measuring actual machine test data at a time when the drivingoperation condition and the environment condition are provided to anactual machine which forms a subject of the virtual prototype, andre-identification means of the virtual prototype, the evaluation meanscomparing an output of the measurement means and the observation result,and making the re-identification means re-identify the virtual prototypeaccording to the comparison result.
 18. The virtual testing system asclaimed in claim 17 wherein a fixed virtual prototype is incorporatedinto a part of a drive system and a load system connected to the part asthe virtual prototype, the testing means perform a test corresponding tothe fixed virtual prototype, and the evaluation means at this time makethe re-identification means perform a re-identification according to thecomparison result.